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--- !u!114 &528506632 MonoBehaviour: @@ -10305,7 +10305,7 @@ RectTransform: m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} m_AnchoredPosition: {x: 230.26001, y: -140} - m_SizeDelta: {x: 25.123747, y: 23.264215} + m_SizeDelta: {x: 27.537466, y: 23.537466} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &568499058 MonoBehaviour: @@ -10379,7 +10379,7 @@ RectTransform: m_LocalEulerAnglesHint: {x: 0, y: 0, z: 0} m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} - m_AnchoredPosition: {x: -128.79599, y: 0} + m_AnchoredPosition: {x: -124.56059, y: 0} m_SizeDelta: {x: 20, y: 20} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &571903142 @@ -10455,7 +10455,7 @@ RectTransform: m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} m_AnchoredPosition: {x: -0, y: 0} - m_SizeDelta: {x: 17.123747, y: 19.264215} + m_SizeDelta: {x: 19.537466, y: 19.537466} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &591203362 MonoBehaviour: @@ -10536,7 +10536,7 @@ RectTransform: m_AnchorMin: {x: 1, y: 0.5} m_AnchorMax: {x: 1, y: 0.5} m_AnchoredPosition: {x: -553.2, y: -93} - m_SizeDelta: {x: 25.123747, y: 23.264215} + m_SizeDelta: {x: 29.313599, y: 23.537466} m_Pivot: {x: 1, y: 0.5} --- !u!114 &595285977 MonoBehaviour: @@ -10610,7 +10610,7 @@ RectTransform: m_LocalEulerAnglesHint: {x: 0, y: 0, z: 0} m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} - m_AnchoredPosition: {x: -18.561874, y: 0} + m_AnchoredPosition: {x: -19.768734, y: 0} m_SizeDelta: {x: 20, y: 20} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &600869056 @@ -10686,7 +10686,7 @@ RectTransform: m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} m_AnchoredPosition: {x: -0, y: 0} - m_SizeDelta: {x: 17.123747, y: 19.264215} + m_SizeDelta: {x: 19.537466, y: 19.537466} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &602372912 MonoBehaviour: @@ -10775,8 +10775,8 @@ RectTransform: - {fileID: 545189441} - {fileID: 890182309} - {fileID: 1876561007} - - {fileID: 855143409} - {fileID: 2037666613} + - {fileID: 855143409} m_Father: {fileID: 268923344} m_RootOrder: 1 m_LocalEulerAnglesHint: {x: 0, y: 0, z: -90} @@ -12123,7 +12123,7 @@ RectTransform: m_Children: - {fileID: 2088978190} m_Father: {fileID: 1213129649} - m_RootOrder: 16 + m_RootOrder: 17 m_LocalEulerAnglesHint: {x: 0, y: 0, z: 0} m_AnchorMin: {x: 0, y: 1} m_AnchorMax: {x: 0, y: 1} @@ -12185,7 +12185,7 @@ MonoBehaviour: NumStackedVectorObservations: 1 m_ActionSpec: m_NumContinuousActions: 1 - BranchSizes: 0100000001000000010000000100000001000000 + BranchSizes: 030000000300000002000000 VectorActionSize: VectorActionDescriptions: [] VectorActionSpaceType: 0 @@ -12219,6 +12219,7 @@ MonoBehaviour: thisCam: {fileID: 1633276149} PlayerController: {fileID: 629870342} enemyPrefab: {fileID: 8240453774489061354, guid: c044b8f18a5ed2f4eaabb8d40095018f, type: 3} + cameraChangerOBJ: {fileID: 317534021} nonRewardDefault: -0.05 shootRewardDefault: -0.1 shootWithoutReadyRewardDefault: -1 @@ -12248,6 +12249,7 @@ MonoBehaviour: mouseXSensitivity: 100 mouseYSensitivity: 200 yRotation: 0.1 + defaultTPCamera: 1 --- !u!114 &629870341 MonoBehaviour: m_ObjectHideFlags: 0 @@ -12566,7 +12568,7 @@ RectTransform: m_LocalEulerAnglesHint: {x: 0, y: 0, z: 0} m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} - m_AnchoredPosition: {x: -18.561874, y: 0} + m_AnchoredPosition: {x: -19.768734, y: 0} m_SizeDelta: {x: 20, y: 20} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &645014597 @@ -12962,7 +12964,7 @@ RectTransform: m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} m_AnchoredPosition: {x: -0, y: 0} - m_SizeDelta: {x: 17.123747, y: 19.264215} + m_SizeDelta: {x: 19.537466, y: 19.537466} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &649944399 MonoBehaviour: @@ -13313,7 +13315,7 @@ RectTransform: m_AnchorMin: {x: 1, y: 0.5} m_AnchorMax: {x: 1, y: 0.5} m_AnchoredPosition: {x: -553.2, y: 37} - m_SizeDelta: {x: 25.123747, y: 23.264215} + m_SizeDelta: {x: 29.313599, y: 23.537466} m_Pivot: {x: 1, y: 0.5} --- !u!114 &691081144 MonoBehaviour: @@ -13567,7 +13569,7 @@ RectTransform: 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m_AnchoredPosition: {x: -93.37999, y: -140} - m_SizeDelta: {x: 25.123747, y: 23.264215} + m_SizeDelta: {x: 27.537466, y: 23.537466} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &808684900 MonoBehaviour: @@ -15658,7 +15660,7 @@ RectTransform: m_LocalEulerAnglesHint: {x: 0, y: 0, z: 0} m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} - m_AnchoredPosition: {x: -119.16389, y: 0} + m_AnchoredPosition: {x: -116.56799, y: 0} m_SizeDelta: {x: 20, y: 20} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &815597367 @@ -15894,7 +15896,7 @@ RectTransform: m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} m_AnchoredPosition: {x: -0, y: 0} - m_SizeDelta: {x: 17.123747, y: 19.264215} + m_SizeDelta: {x: 19.537466, y: 19.537466} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &838006002 MonoBehaviour: @@ -16108,7 +16110,7 @@ RectTransform: m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} m_AnchoredPosition: {x: -201.26, y: -140} - m_SizeDelta: {x: 25.123747, y: 23.264215} + m_SizeDelta: {x: 27.537466, y: 23.537466} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &844985475 MonoBehaviour: @@ -16412,7 +16414,7 @@ RectTransform: m_Children: - {fileID: 135550329} m_Father: {fileID: 611641053} - m_RootOrder: 16 + m_RootOrder: 17 m_LocalEulerAnglesHint: {x: 0, y: 0, z: 0} m_AnchorMin: {x: 0, y: 1} m_AnchorMax: {x: 0, y: 1} @@ -16803,7 +16805,7 @@ RectTransform: m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} m_AnchoredPosition: {x: -0, y: 0} - m_SizeDelta: {x: 17.123747, y: 19.264215} + m_SizeDelta: {x: 19.537466, y: 19.537466} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &878929885 MonoBehaviour: @@ -16884,7 +16886,7 @@ RectTransform: m_AnchorMin: {x: 1, y: 0.5} m_AnchorMax: {x: 1, y: 0.5} m_AnchoredPosition: {x: -553.2, y: 27} - m_SizeDelta: {x: 25.123747, y: 23.264215} + m_SizeDelta: {x: 29.313599, y: 23.537466} m_Pivot: {x: 1, y: 0.5} --- !u!114 &885432328 MonoBehaviour: @@ -17037,7 +17039,7 @@ RectTransform: m_LocalEulerAnglesHint: {x: 0, y: 0, z: 0} m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} - m_AnchoredPosition: {x: -18.561874, y: 0} + m_AnchoredPosition: {x: -20.6568, y: 0} m_SizeDelta: {x: 20, y: 20} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &886618692 @@ -17151,7 +17153,7 @@ RectTransform: m_Children: - {fileID: 453040567} m_Father: {fileID: 201489786} - m_RootOrder: 16 + m_RootOrder: 17 m_LocalEulerAnglesHint: {x: 0, y: 0, z: 0} m_AnchorMin: {x: 0, y: 1} m_AnchorMax: {x: 0, y: 1} @@ -17195,7 +17197,7 @@ RectTransform: m_AnchorMin: {x: 1, y: 0.5} m_AnchorMax: {x: 1, y: 0.5} m_AnchoredPosition: {x: -553.2, y: -123} - m_SizeDelta: {x: 16.561874, y: 23.264215} + m_SizeDelta: {x: 18.6568, y: 23.537466} m_Pivot: {x: 1, y: 0.5} --- !u!114 &894156495 MonoBehaviour: @@ -17432,7 +17434,7 @@ RectTransform: m_AnchorMin: {x: 1, y: 0.5} m_AnchorMax: {x: 1, y: 0.5} m_AnchoredPosition: {x: -553.2, y: -27} - m_SizeDelta: {x: 25.123747, y: 23.264215} + m_SizeDelta: {x: 29.313599, y: 23.537466} m_Pivot: {x: 1, y: 0.5} --- !u!114 &907816954 MonoBehaviour: @@ 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m_AnchoredPosition: {x: -117.02342, y: 0} + m_AnchoredPosition: {x: -114.79186, y: 0} m_SizeDelta: {x: 20, y: 20} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &951578500 @@ -18209,7 +18211,7 @@ RectTransform: m_LocalEulerAnglesHint: {x: 0, y: 0, z: 0} m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} - m_AnchoredPosition: {x: -18.561874, y: 0} + m_AnchoredPosition: {x: -20.6568, y: 0} m_SizeDelta: {x: 20, y: 20} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &956206175 @@ -18447,7 +18449,7 @@ RectTransform: m_AnchorMin: {x: 1, y: 0.5} m_AnchorMax: {x: 1, y: 0.5} m_AnchoredPosition: {x: -553.2, y: 101} - m_SizeDelta: {x: 25.123747, y: 23.264215} + m_SizeDelta: {x: 29.313599, y: 23.537466} m_Pivot: {x: 1, y: 0.5} --- !u!114 &962451838 MonoBehaviour: @@ -18524,7 +18526,7 @@ RectTransform: m_AnchorMin: {x: 1, y: 0.5} m_AnchorMax: {x: 1, y: 0.5} m_AnchoredPosition: {x: -553.2, y: 27} - m_SizeDelta: {x: 25.123747, y: 23.264215} + m_SizeDelta: {x: 29.313599, y: 23.537466} m_Pivot: {x: 1, y: 0.5} --- 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a/Aimbot-PPO-MultiScene/Assets/Script/InGame/AgentWithGun.cs b/Aimbot-PPO-MultiScene/Assets/Script/InGame/AgentWithGun.cs index 7464bf9..0f7bbd5 100644 --- a/Aimbot-PPO-MultiScene/Assets/Script/InGame/AgentWithGun.cs +++ b/Aimbot-PPO-MultiScene/Assets/Script/InGame/AgentWithGun.cs @@ -21,6 +21,8 @@ public class AgentWithGun : Agent public Camera thisCam; public CharacterController PlayerController; public GameObject enemyPrefab; + public GameObject cameraChangerOBJ; + [Header("Rewards")] [Tooltip("Nothing happened reward")] @@ -41,7 +43,7 @@ public class AgentWithGun : Agent [Header("Env")] public bool lockMouse = false; public float Damage = 50; // damage to enemy - public float fireRate = 0.5f; + public float fireRate = 0.5f; public int enemyNum = 3; public int timeLimit = 30; public bool lockCameraX = false; @@ -76,12 +78,15 @@ public class AgentWithGun : Agent private string LoadDirTime; private float LoadDirDateF; private float loadDirTimeF; + public bool defaultTPCamera = true; private StartSeneData DataTransfer; private UIController UICon; private HistoryRecorder HistoryRec; private RaySensors rayScript; + private CameraChange camChanger; - [System.NonSerialized]public float nonReward; + + [System.NonSerialized] public float nonReward; [System.NonSerialized] public float shootReward; [System.NonSerialized] public float shootWithoutReadyReward; [System.NonSerialized] public float hitReward; @@ -118,6 +123,8 @@ public class AgentWithGun : Agent killRewardDefault = DataTransfer.killReward; winRewardDefault = DataTransfer.winReward; loseRewardDefault = DataTransfer.loseReward; + lockMouse = DataTransfer.lockMouse; + defaultTPCamera = DataTransfer.defaultTPCamera; // change Decision Period & Take Actions Between Decisions transform.GetComponent().DecisionPeriod = DataTransfer.DecisionPeriod; @@ -156,6 +163,7 @@ public class AgentWithGun : Agent UICon = transform.GetComponent(); HistoryRec = transform.GetComponent(); rayScript = GetComponent(); + camChanger = cameraChangerOBJ.GetComponent(); // give default Reward to Reward value will be used. nonReward = nonRewardDefault; @@ -167,6 +175,15 @@ public class AgentWithGun : Agent killReward = killRewardDefault; //initialize remainTime remainTime = (int)(timeLimit - Time.time + startTime); + // change default camera view + if (defaultTPCamera) + { + camChanger.ShowTPSView(); + } + else + { + camChanger.ShowFPSView(); + } } } @@ -176,7 +193,7 @@ public class AgentWithGun : Agent //Debug.Log(RaySensors.rayTagResult[0]); }*/ /* ----------此Update用于debug，Build前删除或注释掉！----------*/ - + // --------------初始化--------------- // randomInitEnemys随机生成enemy public void randomInitEnemys(int EnemyNum) @@ -203,27 +220,9 @@ public class AgentWithGun : Agent // ------------动作处理-------------- // moveAgent 用于模拟Input.GetAxis移动 - public void moveAgent(int kW, int kS,int kA,int kD) + public void moveAgent(int vertical, int horizontal) { Vector3 thisMovement; - int horizontal = 0; - int vertical = 0; - if (kW==1 && kS != 1) - { - vertical = 1; - } - else if (kS==1 && kW!=1) - { - vertical = -1; - } - if (kD==1 && kA!=1) - { - horizontal = 1; - } - else if (kA ==1 && kD!=1) - { - horizontal = -1; - } if (horizontal != 0)//当按下按键（水平方向） { @@ -295,7 +294,7 @@ public class AgentWithGun : Agent // ------------动作处理-------------- // cameraControl 用于控制Agent视角转动 - public void cameraControl(float Mouse_X,float Mouse_Y) + public void cameraControl(float Mouse_X, float Mouse_Y) { //Mouse_X = Input.GetAxis("Mouse X") * MouseSensitivity * Time.deltaTime; //Debug.Log(Input.GetAxis("Mouse X")); @@ -359,11 +358,11 @@ public class AgentWithGun : Agent RaycastHit hit; Debug.DrawRay(ray.origin, ray.direction * 100, Color.blue); bool isGunReady = gunReady(); - UICon.updateShootKeyViewer(shoot,isGunReady); + UICon.updateShootKeyViewer(shoot, isGunReady); //按下鼠标左键 if (shoot != 0 && isGunReady == true) { - + lastShootTime = Time.time; if (Physics.Raycast(ray, out hit, 100)) { @@ -420,12 +419,12 @@ public class AgentWithGun : Agent { GameObject[] EnemyGameObjs; EnemyGameObjs = GameObject.FindGameObjectsWithTag("Enemy"); - if(EnemyGameObjs.Length <= 1) + if (EnemyGameObjs.Length <= 1) { //成功击杀所有Enemy return 1; } - else if(Time.time - startTime >= timeLimit) + else if (Time.time - startTime >= timeLimit) { //超时失败 return 2; @@ -477,9 +476,9 @@ public class AgentWithGun : Agent { float epreward = 0f; // 击杀reward判断 - if(enemyKillCount > 0) + if (enemyKillCount > 0) { - for(int i = 0;i < enemyKillCount; i++) + for (int i = 0; i < enemyKillCount; i++) { epreward += killReward; } @@ -506,7 +505,7 @@ public class AgentWithGun : Agent } if (lockMouse) { - Cursor.lockState = CursorLockMode.Locked; // 隐藏并且锁定鼠标 + Cursor.lockState = CursorLockMode.Locked; // hide and lock the mouse } //iniCharts(); thisAgentObj.name = thisAgentObj.GetInstanceID().ToString(); @@ -549,35 +548,26 @@ public class AgentWithGun : Agent public override void OnActionReceived(ActionBuffers actionBuffers) { //获取输入 - int kW = actionBuffers.DiscreteActions[0]; - int kS = actionBuffers.DiscreteActions[1]; - int kA = actionBuffers.DiscreteActions[2]; - int kD = actionBuffers.DiscreteActions[3]; - int mouseShoot = actionBuffers.DiscreteActions[4]; + int vertical = actionBuffers.DiscreteActions[0]; + int horizontal = actionBuffers.DiscreteActions[1]; + int mouseShoot = actionBuffers.DiscreteActions[2]; float Mouse_X = actionBuffers.ContinuousActions[0]; - //float Mouse_Y = actionBuffers.ContinuousActions[1]; - //int timeLimitControl = (int)actionBuffers.ContinuousActions[2]; - //float nonRewardIn = actionBuffers.ContinuousActions[1]; - //float shootRewardIn = actionBuffers.ContinuousActions[2]; - //float shootWithoutReadyRewardIn = actionBuffers.ContinuousActions[3]; - //float hitRewardIn = actionBuffers.ContinuousActions[4]; - //float winRewardIn = actionBuffers.ContinuousActions[5]; - // loseRewardIn = actionBuffers.ContinuousActions[6]; - //float killRewardIn = actionBuffers.ContinuousActions[7]; - //Rewards Update + if (vertical == 2) vertical = -1; + if (horizontal == 2) horizontal = -1; remainTime = (int)(timeLimit - Time.time + startTime); //应用输入 shoot = mouseShoot; - HistoryRec.realTimeKeyCounter(kW, kS, kA, kD, shoot); + HistoryRec.realTimeKeyCounter(vertical, horizontal, shoot); (int kWCount, int kSCount, int kACount, int kDCount, int shootCount) = HistoryRec.getKeyCount(); UICon.updateRemainTime(remainTime); - UICon.updateWASDKeyViewer(kW, kS, kA, kD); + UICon.updateRemainEnemy(enemyNum); + UICon.updateWASDKeyViewer(vertical, horizontal); UICon.updateKeyCounterChart(kWCount, kSCount, kACount, kDCount, shootCount); UICon.updateMouseMovementViewer(Mouse_X); UICon.updateRewardViewer(nonReward, shootReward, shootWithoutReadyReward, hitReward, winReward, loseReward, killReward); cameraControl(Mouse_X, 0); - moveAgent(kW, kS, kA, kD); + moveAgent(vertical, horizontal); float thisRoundReward = rewardCalculate(); //判断结束 @@ -595,7 +585,7 @@ public class AgentWithGun : Agent Debug.Log("reward = " + winReward); EndEpisode(); } - else if(finished == 2) + else if (finished == 2) { //Lose Finished HistoryRec.addRealTimeReward(loseReward); @@ -628,37 +618,45 @@ public class AgentWithGun : Agent ActionSegment continuousActions = actionsOut.ContinuousActions; ActionSegment discreteActions = actionsOut.DiscreteActions; - int kW = 0; - int kS = 0; - int kA = 0; - int kD = 0; - if (Input.GetKey(KeyCode.W)) + int vertical = 0; + int horizontal = 0; + if (Input.GetKey(KeyCode.W) && !Input.GetKey(KeyCode.S)) { - kW = 1; + vertical = 1; } - if (Input.GetKey(KeyCode.S)) + else if (Input.GetKey(KeyCode.S) && !Input.GetKey(KeyCode.W)) { - kS = 1; + vertical = -1; } - if (Input.GetKey(KeyCode.A)) + else { - kA = 1; + vertical = 0; } - if (Input.GetKey(KeyCode.D)) + if (Input.GetKey(KeyCode.D) && !Input.GetKey(KeyCode.A)) { - kD = 1; + horizontal = 1; + } + else if (Input.GetKey(KeyCode.A) && !Input.GetKey(KeyCode.D)) + { + horizontal = -1; + } + else + { + horizontal = 0; } - discreteActions[0] = kW; - discreteActions[1] = kS; - discreteActions[2] = kA; - discreteActions[3] = kD; if (Input.GetMouseButton(0)) { // Debug.Log("mousebuttonhit"); shoot = 1; } - discreteActions[4] = shoot; + else + { + shoot = 0; + } + discreteActions[0] = vertical; + discreteActions[1] = horizontal; + discreteActions[2] = shoot; //^^^^^^^^^^^^^^^^^^^^^discrete-Control^^^^^^^^^^^^^^^^^^^^^^ //vvvvvvvvvvvvvvvvvvvvvvvvvvvvvcontinuous-Controlvvvvvvvvvvvvvvvvvvvvvv diff --git a/Aimbot-PPO-MultiScene/Assets/Script/InGame/HistoryRecorder.cs b/Aimbot-PPO-MultiScene/Assets/Script/InGame/HistoryRecorder.cs index 30911be..7b1e9a0 100644 --- a/Aimbot-PPO-MultiScene/Assets/Script/InGame/HistoryRecorder.cs +++ b/Aimbot-PPO-MultiScene/Assets/Script/InGame/HistoryRecorder.cs @@ -31,24 +31,24 @@ public class HistoryRecorder : MonoBehaviour { EPTotalShootCount.Add(TotalShootCount); } - public void realTimeKeyCounter(int kW, int kS, int kA, int kD, int shoot) + public void realTimeKeyCounter(int vertical, int horizontal, int shoot) { - if (kW == 1) + if (vertical == 1) { realTimeWKeyCount += 1; } - if (kS == 1) + else if (vertical == -1) { realTimeSKeyCount += 1; } - if (kA == 1) - { - realTimeAKeyCount += 1; - } - if (kD == 1) + if (horizontal == 1) { realTimeDKeyCount += 1; } + else if (horizontal == -1) + { + realTimeAKeyCount += 1; + } if (shoot == 1) { realTimeShootCount += 1; diff --git a/Aimbot-PPO-MultiScene/Assets/Script/InGame/UIController.cs b/Aimbot-PPO-MultiScene/Assets/Script/InGame/UIController.cs index f3912f6..5c9d742 100644 --- a/Aimbot-PPO-MultiScene/Assets/Script/InGame/UIController.cs +++ b/Aimbot-PPO-MultiScene/Assets/Script/InGame/UIController.cs @@ -69,39 +69,37 @@ public class UIController : MonoBehaviour } //------------Key Viewer---------- - public void updateWASDKeyViewer(int kW,int kS,int kA,int kD) + public void updateWASDKeyViewer(int vertical,int horizontal) { - if (kW == 1) + if (vertical == 1) { upText.color = Color.red; + downText.color = Color.black; } - else - { - upText.color = Color.black; - } - if (kS == 1) + else if (vertical == -1) { downText.color = Color.red; + upText.color = Color.black; } else { downText.color = Color.black; + upText.color = Color.black; } - if(kA == 1) - { - leftText.color = Color.red; - } - else - { - leftText.color = Color.black; - } - if( kD == 1) + if (horizontal == 1) { rightText.color = Color.red; + leftText.color = Color.black; + } + else if (horizontal == -1) + { + leftText.color = Color.red; + rightText.color = Color.black; } else { - rightText.color = Color.black; + downText.color = Color.black; + upText.color = Color.black; } } public void updateShootKeyViewer(int shoot,bool isGunReady) diff --git a/Aimbot-PPO-MultiScene/Assets/Script/InGame/gameFlowController.cs b/Aimbot-PPO-MultiScene/Assets/Script/InGame/gameFlowController.cs new file mode 100644 index 0000000..d634ac0 --- /dev/null +++ b/Aimbot-PPO-MultiScene/Assets/Script/InGame/gameFlowController.cs @@ -0,0 +1,27 @@ +using System.Collections; +using System.Collections.Generic; +using UnityEngine; + +public class gameFlowController : MonoBehaviour +{ + public GameObject Agent; + AgentWithGun agentWithGun; + // Start is called before the first frame update + void Start() + { + agentWithGun = Agent.GetComponent(); + } + + // Update is called once per frame + void Update() + { + if (Input.GetKey(KeyCode.Escape)) + { + Application.Quit(); + } + if (Input.GetKey(KeyCode.L)) + { + agentWithGun.lockMouse = !agentWithGun.lockMouse; + } + } +} diff --git a/Aimbot-PPO-MultiScene/Assets/Script/InGame/gameFlowController.cs.meta b/Aimbot-PPO-MultiScene/Assets/Script/InGame/gameFlowController.cs.meta new file mode 100644 index 0000000..6a0dd77 --- /dev/null +++ b/Aimbot-PPO-MultiScene/Assets/Script/InGame/gameFlowController.cs.meta @@ -0,0 +1,11 @@ +fileFormatVersion: 2 +guid: 9a8fb4d12d4b8fc4784f3e142e7fdcf8 +MonoImporter: + externalObjects: {} + serializedVersion: 2 + defaultReferences: [] + executionOrder: 0 + icon: {instanceID: 0} + userData: + assetBundleName: + assetBundleVariant: diff --git a/Aimbot-PPO-MultiScene/Assets/Script/Start/EnvArgsChanger.cs b/Aimbot-PPO-MultiScene/Assets/Script/Start/EnvArgsChanger.cs index 8e63d1f..1755b16 100644 --- a/Aimbot-PPO-MultiScene/Assets/Script/Start/EnvArgsChanger.cs +++ b/Aimbot-PPO-MultiScene/Assets/Script/Start/EnvArgsChanger.cs @@ -19,6 +19,21 @@ public class EnvArgsChanger : MonoBehaviour public Text DecisionPeriodDataText; public Toggle TakeActionsBetweenDecisionsToggle; + [Header("Lock Mouse")] + public Toggle LockMouseToggle; + + [Header("Default Camera")] + public Toggle FPToggle; + public Text FPText; + public Toggle TPToggle; + public Text TPText; + + private StartSeneData startSeneData; + private void Start() + { + startSeneData = DataTransfer.GetComponent(); + } + public void onEnemynumValueChanged() { @@ -30,7 +45,7 @@ public class EnvArgsChanger : MonoBehaviour else { EnemyNumText.color = Color.yellow; - DataTransfer.GetComponent().EnemyNum = Math.Abs(int.Parse(EnemyNumInput.GetComponent().text)); + startSeneData.EnemyNum = Math.Abs(int.Parse(EnemyNumInput.GetComponent().text)); } } @@ -44,19 +59,48 @@ public class EnvArgsChanger : MonoBehaviour else { TimeLimText.color = Color.yellow; - DataTransfer.GetComponent().Timelim = Math.Abs(int.Parse(TimelimInput.GetComponent().text)); + startSeneData.Timelim = Math.Abs(int.Parse(TimelimInput.GetComponent().text)); } } public void onDPSlideValueChanged() { // DecisionPeriod(DP) value Control - DataTransfer.GetComponent().DecisionPeriod = (int)(DecisionPeriodSlide.GetComponent().value); - DecisionPeriodDataText.text = DataTransfer.GetComponent().DecisionPeriod.ToString(); + startSeneData.DecisionPeriod = (int)(DecisionPeriodSlide.GetComponent().value); + DecisionPeriodDataText.text = startSeneData.DecisionPeriod.ToString(); } public void onABDToggleChanged() { // Actions Between Decisions(ABD) Toggle Control - DataTransfer.GetComponent().ActionsBetweenDecisions = TakeActionsBetweenDecisionsToggle.isOn; + startSeneData.ActionsBetweenDecisions = TakeActionsBetweenDecisionsToggle.isOn; + } + public void onLockMouseToggleChanged() + { + // lock mouse or not + startSeneData.lockMouse = LockMouseToggle.isOn; + } + public void onTPCamToggleChanged() + { + startSeneData.defaultTPCamera = true; + + FPToggle.interactable = true; + FPToggle.SetIsOnWithoutNotify(false); + FPText.color = Color.gray; + + TPToggle.SetIsOnWithoutNotify(true); + TPToggle.interactable = false; + TPText.color = Color.green; + } + public void onFPCameToggleChanged() + { + startSeneData.defaultTPCamera = false; + + TPToggle.interactable = true; + TPToggle.SetIsOnWithoutNotify(false); + TPText.color = Color.gray; + + FPToggle.SetIsOnWithoutNotify(true); + FPToggle.interactable = false; + FPText.color = Color.green; } } diff --git a/Aimbot-PPO-MultiScene/Assets/Script/Start/StartSeneData.cs b/Aimbot-PPO-MultiScene/Assets/Script/Start/StartSeneData.cs index cbfa219..1bdf784 100644 --- a/Aimbot-PPO-MultiScene/Assets/Script/Start/StartSeneData.cs +++ b/Aimbot-PPO-MultiScene/Assets/Script/Start/StartSeneData.cs @@ -13,6 +13,8 @@ public class StartSeneData : MonoBehaviour public float killRewardDefault = 10.0f; public float winRewardDefault = 20.0f; public float loseRewardDefault = -10.0f; + public bool lockMouse = false; + public bool defaultTPCamera = true; // LoadDir [System.NonSerialized]public string LoadDirDate = "0"; diff --git a/Aimbot-PPO-Python/DemoRecorder.ipynb b/Aimbot-PPO-Python/DemoRecorder.ipynb new file mode 100644 index 0000000..3ac169a --- /dev/null +++ b/Aimbot-PPO-Python/DemoRecorder.ipynb @@ -0,0 +1,90 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "√√√√√Enviroment Initialized Success√√√√√\n" + ] + } + ], + "source": [ + "import time\n", + "import aimBotEnv\n", + "from HumanAction import HumanActions\n", + "\n", + "# Env\n", + "ENV_PATH = \"./Build-CloseEnemyCut/Aimbot-PPO\"\n", + "WORKER_ID = 1\n", + "BASE_PORT = 200\n", + "\n", + "MOUSEDISCOUNT = 8.0\n", + "MAX_EP = 10000000\n", + "\n", + "env = aimBotEnv.makeEnv(envPath=ENV_PATH, workerID=WORKER_ID, basePort=BASE_PORT)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "UnityCommunicatorStoppedException", + "evalue": "Communicator has exited.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mUnityCommunicatorStoppedException\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_37248/645561173.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mactions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdemoAct\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetHumanActions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mactions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32mc:\\Users\\UCUNI\\OneDrive\\Unity\\ML-Agents\\Aimbot-PPO\\Aimbot-PPO-Python\\aimBotEnv.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, actions, behaviorName, trackedAgent)\u001b[0m\n\u001b[0;32m 72\u001b[0m \u001b[1;31m# take action to env\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 73\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_actions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbehavior_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbehaviorName\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maction\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mthisActionTuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 75\u001b[0m \u001b[1;31m# get nextState & reward & done after this action\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 76\u001b[0m \u001b[0mnextState\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloadDir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaveNow\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetSteps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbehaviorName\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrackedAgent\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\Users\\UCUNI\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\mlagents_envs\\timers.py\u001b[0m in \u001b[0;36mwrapped\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 303\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 304\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mhierarchical_timer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__qualname__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 305\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 306\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 307\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m \u001b[1;31m# type: ignore\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\Users\\UCUNI\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\mlagents_envs\\environment.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 333\u001b[0m \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_communicator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexchange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep_input\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_poll_process\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 334\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0moutputs\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 335\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mUnityCommunicatorStoppedException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Communicator has exited.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 336\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_behavior_specs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 337\u001b[0m \u001b[0mrl_output\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrl_output\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mUnityCommunicatorStoppedException\u001b[0m: Communicator has exited." + ] + } + ], + "source": [ + "done = False\n", + "env.reset()\n", + "demoAct = HumanActions(mouseDiscount=MOUSEDISCOUNT)\n", + "for ep in range(MAX_EP):\n", + " while not done:\n", + " actions = demoAct.getHumanActions()\n", + " env.step(actions=actions)6\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.7 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "86e2db13b09bd6be22cb599ea60c1572b9ef36ebeaa27a4c8e961d6df315ac32" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Aimbot-PPO-Python/HumanAction.py b/Aimbot-PPO-Python/HumanAction.py new file mode 100644 index 0000000..cd29dd4 --- /dev/null +++ b/Aimbot-PPO-Python/HumanAction.py @@ -0,0 +1,51 @@ +import keyboard +import mouse + + +class HumanActions: + def __init__(self, mouseDiscount: float = 10, screenW: int = 1920, screenH: int = 1080): + def multiPressed(): + pass + + keyboard.add_hotkey("w+a", multiPressed) + keyboard.add_hotkey("w+d", multiPressed) + keyboard.add_hotkey("s+a", multiPressed) + keyboard.add_hotkey("s+d", multiPressed) + self.screenW = screenW + self.screenH = screenH + self.MOUSEDISCOUNT = mouseDiscount + + def getHumanActions(self): + x, _ = mouse.get_position() + xMovement = (x - self.screenW / 2) / self.MOUSEDISCOUNT + + ws = 0 + ad = 0 + click = 0 + if keyboard.is_pressed("w"): + ws = 1 + elif keyboard.is_pressed("s"): + ws = 2 + if keyboard.is_pressed("d"): + ad = 1 + elif keyboard.is_pressed("a"): + ad = 2 + if keyboard.is_pressed("w+d"): + ws = 1 + ad = 1 + elif keyboard.is_pressed("w+a"): + ws = 1 + ad = 2 + elif keyboard.is_pressed("s+d"): + ws = 2 + ad = 1 + elif keyboard.is_pressed("s+a"): + ws = 2 + ad = 2 + if mouse.is_pressed(button="left"): + click = 1 + + actions = [ws, ad, click, [xMovement]] + + mouse.move(self.screenW / 2, self.screenH / 2) + return actions diff --git a/Aimbot-PPO-Python/PPO-mian.ipynb b/Aimbot-PPO-Python/PPO-mian.ipynb new file mode 100644 index 0000000..c5f2bda --- /dev/null +++ b/Aimbot-PPO-Python/PPO-mian.ipynb @@ -0,0 +1,1147 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import aimBotEnv\n", + "import PPO\n", + "import numpy as np\n", + "\n", + "import tensorflow as tf\n", + "import time\n", + "import datetime\n", + "\n", + "from PPO import PPO\n", + "from PPOBuffer import PPOBuffer\n", + "from PPOConfig import PPOConfig\n", + "from PPOHistoryRecorder import PPOHistory" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Attempts to allocate only the GPU memory needed for allocation\n", + "physical_devices = tf.config.list_physical_devices(\"GPU\")\n", + "tf.config.experimental.set_memory_growth(physical_devices[0], True)\n", + "tf.random.set_seed(9331)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Env\n", + "ENV_PATH = \"./Build-CloseEnemyCut/Aimbot-PPO\"\n", + "WORKER_ID = 1\n", + "BASE_PORT = 200\n", + "\n", + "MAX_EP = 1000\n", + "EP_LENGTH = 100000\n", + "GAMMA = 0.99 # discount future reward (UP?)\n", + "EPSILON = 0.2 # clip Ratio range[1-EPSILON,1+EPSILON]\n", + "ACTOR_LR = 1e-5 # LR\n", + "CRITIC_LR = 2e-5 # LR\n", + "BATCH = 256 # learning step\n", + "EPOCHS = 8\n", + "ENTROPY_WHEIGHT = 0.001 # sigma's entropy in Actor loss\n", + "ACTION_INTERVAL = 1 # take action every ACTION_INTERVAL steps\n", + "\n", + "\n", + "TRAIN = True\n", + "SAVE_DIR = \"PPO-Model/\" + datetime.datetime.now().strftime(\"%m%d%H%M\") + \"/\"\n", + "LOAD_DIR = None\n", + "\n", + "CTN_ACTION_RANGE = 10\n", + "\n", + "ppoConfig = PPOConfig()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# initialize enviroment & buffer class\n", + "env = aimBotEnv.makeEnv(envPath=ENV_PATH, workerID=WORKER_ID, basePort=BASE_PORT)\n", + "ppoBuffer = PPOBuffer()\n", + "ppoHistory = PPOHistory()\n", + "\n", + "STATE_SIZE = env.STATE_SIZE\n", + "DISCRETE_SHAPE = env.DISCRETE_SHAPE\n", + "DISCRETE_SIZE = env.DISCRETE_SIZE\n", + "CONTINUOUS_SIZE = env.CONTINUOUS_SIZE\n", + "ACTSPEC = env.ACTION_SPEC\n", + "_, _, _, loadDir, _ = env.getSteps()\n", + "\n", + "agent = PPO(\n", + " stateSize=STATE_SIZE,\n", + " disActShape=DISCRETE_SHAPE,\n", + " conActSize=CONTINUOUS_SIZE,\n", + " conActRange=CTN_ACTION_RANGE,\n", + " PPOConfig=ppoConfig,\n", + ")\n", + "\n", + "# check load model or not\n", + "if np.any(loadDir == 0):\n", + " # create a new model\n", + " print(\"No loadDir specified,Create a New Model\")\n", + " LOAD_DIR = None\n", + "else:\n", + " # load model\n", + " loadDirDateSTR = str(int(loadDir[0]))\n", + " loadDirTimeSTR = str(int(loadDir[1]))\n", + " if len(loadDirDateSTR) != 8:\n", + " # fill lost 0 while converse float to string\n", + " for _ in range(8 - len(loadDirDateSTR)):\n", + " loadDirDateSTR = \"0\" + loadDirDateSTR\n", + " if len(loadDirTimeSTR) != 6:\n", + " # fill lost 0 while converse float to string\n", + " for _ in range(6 - len(loadDirTimeSTR)):\n", + " loadDirTimeSTR = \"0\" + loadDirTimeSTR\n", + " LOAD_DIR = \"PPO-Model/\" + loadDirDateSTR + \"/\" + loadDirTimeSTR\n", + " print(\"Load Model:\")\n", + " print(LOAD_DIR)\n", + "\n", + "print(\"CONTINUOUS_SIZE\", CONTINUOUS_SIZE)\n", + "print(\"DISCRETE_SIZE\", DISCRETE_SIZE)\n", + "print(\"STATE_SIZE\", STATE_SIZE)\n", + "\n", + "disActShape = [3, 3, 2]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EP 0 START\n", + "[0, 1, 0, array([-9.97837827])]\n", + "[0, 1, 0, array([-9.98691182])]\n", + "[0, 1, 0, array([-10.])]\n", + "[0, 1, 0, array([-9.64988654])]\n", + "[0, 1, 0, array([-7.57418975])]\n", + "[0, 1, 0, array([-5.92892356])]\n", + "[0, 1, 0, array([-6.17624156])]\n", + "[0, 1, 0, array([-2.24651228])]\n", + "[0, 1, 1, array([-4.46437881])]\n", + "[0, 1, 0, array([-3.43449784])]\n", + "[0, 1, 0, array([-0.60291197])]\n", + "[0, 1, 0, array([0.08531085])]\n", + "[0, 1, 0, array([-0.45389164])]\n", + "[0, 1, 0, array([0.37792929])]\n", + 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If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " return self.standDims(np.asarray(self.actions))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 0, array([10.])]\n", + "[0, 1, 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immediately?\n", + " saveNow = 0\n", + "\n", + " epTotalReward = 0\n", + " entropys = []\n", + "\n", + " while not done:\n", + " step += 1\n", + "\n", + " actions, predictResult = agent.chooseAction(s)\n", + " print(actions)\n", + " avrEntropy, _, _ = agent.getAverageEntropy(predictResult)\n", + " nextState, thisReward, done, _, saveNow = env.step(actions=actions)\n", + "\n", + " entropys.append(avrEntropy)\n", + " ppoBuffer.saveBuffers(\n", + " state=s, actorProb=predictResult, action=actions, reward=thisReward, done=done\n", + " )\n", + " epTotalReward += thisReward\n", + "\n", + " nextState = nextState.reshape([STATE_SIZE])\n", + " s = nextState\n", + "\n", + " if done:\n", + " print(\"EP OVER!\")\n", + " if saveNow != 0:\n", + " print(\"SAVENOW!\")\n", + " saveNow = 0\n", + " agent.saveWeights()\n", + " # update PPO after Batch step or GameOver\n", + " if (step + 1) % BATCH == 0 or done:\n", + " if TRAIN:\n", + " actorLosses, criticLosses = agent.trainCritcActor(\n", + " states=ppoBuffer.getStates(),\n", + " oldActorResult=ppoBuffer.getActorProbs(),\n", + " actions=ppoBuffer.getActions(),\n", + " rewards=ppoBuffer.getRewards(),\n", + " dones=ppoBuffer.getDones(),\n", + " nextState=nextState,\n", + " epochs=EPOCHS,\n", + " )\n", + " ppoBuffer.clearBuffer()\n", + " ppoHistory.saveHis(epTotalReward, np.mean(entropys), actorLosses, criticLosses)\n", + "\n", + " if epTotalReward > maxTotalReward and epTotalReward != 0:\n", + " maxTotalReward = epTotalReward\n", + " agent.saveWeights(epTotalReward)\n", + " print(\"New Record! Save NN\", epTotalReward)\n" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "86e2db13b09bd6be22cb599ea60c1572b9ef36ebeaa27a4c8e961d6df315ac32" + }, + "kernelspec": { + "display_name": "Python 3.9.7 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Aimbot-PPO-Python/PPO.py b/Aimbot-PPO-Python/PPO.py index e0d19d6..fb952e3 100644 --- a/Aimbot-PPO-Python/PPO.py +++ b/Aimbot-PPO-Python/PPO.py @@ -1,108 +1,213 @@ import tensorflow as tf +from tensorflow.python.ops.numpy_ops import ndarray import tensorflow_probability as tfp import numpy as np +import time import math -import copy + import datetime -import os +from PPOConfig import PPOConfig from tensorflow import keras from tensorflow.keras import layers from tensorflow.keras import optimizers -from keras_radam import RAdam + +EPS = 1e-10 class PPO(object): - """Create PPO Agent - """ + def __init__( + self, + stateSize: int, + disActShape: list, + conActSize: int, + conActRange: float, + PPOConfig: PPOConfig, + ): + """initialize PPO + + Args: + stateSize (int): enviroment state size + disActShape (numpy): discrete Action shape. + just like [3,2],means 2 type of dis actions,each act include 3 and 2 types + if no discrete action output then use [0]. + conActSize (int): continuous Action Size. if no continuous action output then use 0. + conActRange (float): continuous action range. -conActRange to +conActRange + PPOConfig (PPOConfig): PPO configuration + """ + # check use dis action or not. + if disActShape == [0]: + # non dis action output + self.disActSize = 0 + self.disOutputSize = 0 + else: + # make sure disActShape greater than 1 + try: + if np.any(np.array(disActShape) <= 1): + raise ValueError( + "disActShape error,disActShape should greater than 1 but get", disActShape + ) + except ValueError: + raise + self.disActSize = len(disActShape) + self.disOutputSize = sum(disActShape) - def __init__(self, stateSize, disActShape, conActSize, conActRange, criticLR, actorLR, gamma, epsilon, entropyWeight, saveDir, loadModelDir): - - # check disActShape is correct(greater than 1) - try: - if np.any(np.array(disActShape)<=1): - raise ValueError("disActShape error,disActShape should greater than 1 but get",disActShape) - except ValueError as e: - raise - self.stateSize = stateSize - # self.actionSize = actionSize - self.disActShape = disActShape # shape of discrete action output. like [3,3,2] - self.disActSize = len(disActShape) + self.disActShape = disActShape self.conActSize = conActSize self.conActRange = conActRange - self.criticLR = criticLR - self.actorLR = actorLR - self.GAMMA = gamma - self.EPSILON = epsilon - self.saveDir = saveDir - self.entropyWeight = entropyWeight + self.muSigSize = 2 + self.conOutputSize = conActSize * self.muSigSize - self.disOutputSize = sum(disActShape) - self.conOutputSize = conActSize * 2 - - if loadModelDir == None: + # config + self.NNShape = PPOConfig.NNShape + self.criticLR = PPOConfig.criticLR + self.actorLR = PPOConfig.actorLR + self.gamma = PPOConfig.gamma + self.lmbda = PPOConfig.lmbda + self.clipRange = PPOConfig.clipRange + self.entropyWeight = PPOConfig.entropyWeight + self.trainEpochs = PPOConfig.trainEpochs + self.saveDir = PPOConfig.saveDir + self.loadModelDir = PPOConfig.loadModelDir + print("---------thisPPO Params---------") + print("self.stateSize = ", self.stateSize) + print("self.disActShape = ", self.disActShape) + print("self.disActSize", self.disActSize) + print("self.disOutputSize", self.disOutputSize) + print("self.conActSize = ", self.conActSize) + print("self.conActRange = ", self.conActRange) + print("self.conOutputSize = ", self.conOutputSize) + + # config + print("---------thisPPO config---------") + print("self.NNShape = ", self.NNShape) + print("self.criticLR = ", self.criticLR) + print("self.actorLR = ", self.actorLR) + print("self.gamma = ", self.gamma) + print("self.lmbda = ", self.lmbda) + print("self.clipRange = ", self.clipRange) + print("self.entropyWeight = ", self.entropyWeight) + print("self.trainEpochs = ", self.trainEpochs) + print("self.saveDir = ", self.saveDir) + print("self.loadModelDir = ", self.loadModelDir) + + # load NN or not + if self.loadModelDir is None: # critc NN - self.critic = self.buildCriticNet(self.stateSize, 1, compileModel = True) + self.critic = self.buildCriticNet(self.stateSize, 1, compileModel=True) # actor NN - self.actor = self.buildActorNet(self.stateSize, self.conActRange, compileModel = True) + self.actor = self.buildActorNet(self.stateSize, compileModel=True) + print("---------Actor Model Create Success---------") + self.actor.summary() + print("---------Critic Model Create Success---------") + self.critic.summary() else: # critc NN self.critic = self.buildCriticNet(self.stateSize, 1, compileModel=True) # actor NN - self.actor = self.buildActorNet(self.stateSize, self.conActRange, compileModel=True) + self.actor = self.buildActorNet(self.stateSize, compileModel=True) # load weight to Critic&Actor NN - self.loadWeightToModels(loadModelDir) - + self.loadWeightToModels(self.loadModelDir) + print("---------Actor Model Load Success---------") + self.actor.summary() + print("---------Critic Model Load Success---------") + self.critic.summary() # Build Net - def buildActorNet(self, inputSize, continuousActionRange,compileModel): + def buildActorNet(self, inputSize: int, compileModel: bool): """build Actor Nueral Net and compile.Output:[disAct1,disAct2,disAct3,mu,sigma] Args: inputSize (int): InputLayer Nueral size. - continuousActionRange (foat): continuous Action's max Range. + compileModel (bool): compile Model or not. Returns: keras.Model: return Actor NN """ - stateInput = layers.Input(shape=(inputSize,), name='stateInput') - dense0 = layers.Dense(500, activation='relu',name='dense0',)(stateInput) - dense1 = layers.Dense(200, activation='relu',name='dense1',)(dense0) - dense2 = layers.Dense(100, activation='relu', name='dense2')(dense1) + # -----------Input Layers----------- + stateInput = layers.Input(shape=(inputSize,), name="stateInput") - disAct1 = layers.Dense(3, activation='softmax',name='WSAction')(dense2) # WS - disAct2 = layers.Dense(3, activation='softmax',name='ADAction')(dense2) # AD - disAct3 = layers.Dense(2, activation='softmax',name='ShootAction')(dense2) # Mouse shoot - mu = continuousActionRange * layers.Dense(1, activation='tanh', name='muOut')(dense2) # mu，既正态分布mean - sigma = 1e-8 + layers.Dense(1, activation='softplus',name='sigmaOut')(dense2) # sigma，既正态分布 - # musig = layers.concatenate([mu,sigma],name = 'musig') - totalOut = layers.concatenate( - [disAct1, disAct2, disAct3, mu, sigma], name='totalOut') # package + # -------Intermediate layers-------- + interLayers = [] + interLayersIndex = 0 + for neuralUnit in self.NNShape: + thisLayerName = "dense" + str(interLayersIndex) + if interLayersIndex == 0: + interLayers.append( + layers.Dense(neuralUnit, activation="relu", name=thisLayerName)(stateInput) + ) + else: + interLayers.append( + layers.Dense(neuralUnit, activation="relu", name=thisLayerName)(interLayers[-1]) + ) + interLayersIndex += 1 + # ----------Output Layers----------- + outputLayersList = [] + if self.disActSize != 0: + # while NN have discrete action output. + disActIndex = 0 + for thisDisActDepth in self.disActShape: + thisDisActName = "disAct" + str(disActIndex) + outputLayersList.append( + layers.Dense(thisDisActDepth, activation="softmax", name=thisDisActName)( + interLayers[-1] + ) + ) + disActIndex += 1 + if self.conActSize != 0: + # while NN have continuous action output. + mu = tf.multiply( + layers.Dense(1, activation="tanh", name="muOut")(interLayers[-1]), self.conActRange + ) # mu，既正态分布位置参数 + sigma = tf.add( + layers.Dense(1, activation="softplus", name="sigmaOut")(interLayers[-1]), EPS + ) # sigma，既正态分布尺度参数 + outputLayersList.append(mu) + outputLayersList.append(sigma) + totalOut = layers.concatenate(outputLayersList, name="totalOut") # package + + # ----------Model Compile----------- model = keras.Model(inputs=stateInput, outputs=totalOut) - #actorOPT = optimizers.Adam(learning_rate = self.actorLR) - if compileModel: - actorOPT = RAdam(self.actorLR) + if compileModel: # Compile Model + actorOPT = optimizers.Adam(learning_rate=self.actorLR) model.compile(optimizer=actorOPT, loss=self.aLoss()) return model - def buildCriticNet(self, inputSize, outputSize,compileModel): + def buildCriticNet(self, inputSize: int, outputSize: int, compileModel: bool): """build Critic Nueral Net and compile.Output:[Q] Args: - inputSize (int): InputLayer Neural Size - outputSize (float): Q size + inputSize (int): input size + outputSize (int): output size + compileModel (bool): compile Model or not. Returns: keras.Model: return Critic NN """ - stateInput = keras.Input(shape=(inputSize,)) - dense0 = layers.Dense(500, activation='relu', - name='dense0',)(stateInput) - dense1 = layers.Dense(200, activation='relu')(dense0) - dense2 = layers.Dense(100, activation='relu')(dense1) - output = layers.Dense(outputSize)(dense2) + # -----------Input Layers----------- + stateInput = keras.Input(shape=(inputSize,), name="stateInput") + + # -------Intermediate layers-------- + interLayers = [] + interLayersIndex = 0 + for neuralUnit in self.NNShape: + thisLayerName = "dense" + str(interLayersIndex) + if interLayersIndex == 0: + interLayers.append( + layers.Dense(neuralUnit, activation="relu", name=thisLayerName)(stateInput) + ) + else: + interLayers.append( + layers.Dense(neuralUnit, activation="relu", name=thisLayerName)(interLayers[-1]) + ) + interLayersIndex += 1 + + # ----------Output Layers----------- + output = layers.Dense(outputSize, activation=None)(interLayers[-1]) + + # ----------Model Compile----------- model = keras.Model(inputs=stateInput, outputs=output) if compileModel: criticOPT = optimizers.Adam(learning_rate=self.criticLR) @@ -110,39 +215,53 @@ class PPO(object): return model # loss Function + # critic loss def cLoss(self): - """Critic Loss function - """ + """Critic Loss function""" + def loss(y_true, y_pred): # y_true: discountedR # y_pred: critcV = model.predict(states) - advantage = y_true - y_pred # TD error - loss = tf.reduce_mean(tf.square(advantage)) + adv = y_true - y_pred # TD error + loss = tf.reduce_mean(tf.square(adv)) return loss + return loss + # actor loss def aLoss(self): - def getDiscreteALoss(nowProbs,oldProbs,advantage): + """Actor Loss function""" + + def getDiscreteALoss(nowProbs, oldProbs, disOneHotAct, actShape, advantage): """get Discrete Action Loss Args: - nowProbs (tf.constant): (length,actionSize) - oldProbs (tf.constant): (length,actionSize) + nowProbs (tf.constant): (length,actionProbSize) + oldProbs (tf.constant): (length,actionProbSize) advantage (tf.constant): (length,) Returns: tf.constant: (length,) """ - entropy = tf.reduce_mean(tf.math.multiply(nowProbs,tf.math.log(nowProbs+1e-6))) - ratio = tf.math.divide(nowProbs,oldProbs+1e-6) - value = tf.math.multiply(ratio,tf.expand_dims(advantage,axis = 1)) - clipRatio = tf.clip_by_value(ratio,1. - self.EPSILON,1.+self.EPSILON) - clipValue = tf.math.multiply(clipRatio,tf.expand_dims(advantage,axis = 1)) - loss = -tf.reduce_mean(tf.math.minimum(value,clipValue)) + self.entropyWeight * entropy + entropy = tf.negative( + tf.reduce_mean(tf.math.multiply(nowProbs, tf.math.log(nowProbs + EPS))) + ) + nowSingleProbs = tf.reduce_mean(tf.multiply(nowProbs, disOneHotAct), axis=1) + nowSingleProbs = tf.multiply(nowSingleProbs, actShape) + oldSingleProbs = tf.reduce_mean(tf.multiply(oldProbs, disOneHotAct), axis=1) + oldSingleProbs = tf.multiply(oldSingleProbs, actShape) + ratio = tf.math.divide(nowSingleProbs, oldSingleProbs + EPS) + value = tf.math.multiply(ratio, advantage) + clipRatio = tf.clip_by_value(ratio, 1.0 - self.clipRange, 1.0 + self.clipRange) + clipValue = tf.math.multiply(clipRatio, advantage) + loss = tf.math.negative( + tf.reduce_mean(tf.math.minimum(value, clipValue)) + - tf.multiply(self.entropyWeight, entropy) + ) return loss - - def getContinuousALoss(musig,actions,oldProbs,advantage): + + def getContinuousALoss(musig, actions, oldProbs, advantage): """get Continuous Action Loss Args: @@ -154,150 +273,376 @@ class PPO(object): Returns: tf.constant: (length,) """ - mu = musig[:,0] - sigma = musig[:,1] - dist = tfp.distributions.Normal(mu,sigma) - + mu = musig[:, 0] + sigma = musig[:, 1] + dist = tfp.distributions.Normal(mu, sigma) + nowProbs = dist.prob(actions) - ratio = tf.math.divide(nowProbs,oldProbs+1e-6) entropy = tf.reduce_mean(dist.entropy()) - - value = tf.math.multiply(ratio,tf.expand_dims(advantage,axis = 1)) - clipValue = tf.clip_by_value(ratio,1. - self.EPSILON,1.+self.EPSILON) * advantage - loss = -tf.reduce_mean(tf.math.minimum(value,clipValue)) + self.entropyWeight * entropy + + ratio = tf.math.divide(nowProbs, oldProbs + EPS) + value = tf.math.multiply(ratio, advantage) + clipRatio = tf.clip_by_value(ratio, 1.0 - self.clipRange, 1.0 + self.clipRange) + clipValue = tf.math.multiply(clipRatio, advantage) + loss = tf.negative( + tf.reduce_mean(tf.math.minimum(value, clipValue)) + - tf.multiply(self.entropyWeight, entropy) + ) return loss def loss(y_true, y_pred): - # y_true: [[disAct1, disAct2, disAct3, mu, sigma]] - # y_pred: muSigma = self.actor(state) = - # [[disAct1, disAct2, disAct3, mu, sigma]] - oldDisProbs = y_true[:,0:self.disOutputSize] - oldConMusigs = y_true[:,self.disOutputSize:self.disOutputSize+self.conActSize] - conActions = y_true[:,self.disOutputSize+self.conActSize:self.disOutputSize+(self.conActSize*2)] - advantage = y_true[:,-1] - - nowDisProbs = y_pred[:,0:self.disOutputSize] # [disAct1, disAct2, disAct3] - nowConMusigs = y_pred[:,self.disOutputSize:] #[musig1,musig2] - - totalALoss = tf.constant([0.]) + # y_true: [[disActProb..., conActProbs..., disOneHotActs..., conAct..., advantage]] + # y_pred: [[disActProb..., mu, sigma...]] + totalALoss = 0 totalActionNum = 0 - - # for nowProb,oldProb in zip(tf.transpose(nowDisProbs,perm=[1,0,2]),tf.transpose(oldDisProbs,perm=[1,0,2])): - lastDisActShape = 0 - for shape in self.disActShape: - thisNowDisProbs = nowDisProbs[:,lastDisActShape:lastDisActShape+shape] - thisOldDisProbs = oldDisProbs[:,lastDisActShape:lastDisActShape+shape] - discreteALoss = getDiscreteALoss(thisNowDisProbs,thisOldDisProbs,advantage) - lastDisActShape += shape - totalALoss += discreteALoss - totalActionNum += 1 - # for nowConMusig,conAction,oldPiProb in zip(tf.transpose(nowConMusigs,perm=[1,0,2]),conActions,oldPiProbs): - lastConAct = 0 - for act in range(self.conActSize): - thisNowConMusig = nowConMusigs[:,lastConAct:lastConAct+((act+1)*2)] - thisOldConMusig = oldConMusigs[:,lastConAct:lastConAct+((act+1)*2)] - thisConAction = conActions[:,act] - continuousAloss = getContinuousALoss(thisNowConMusig,thisConAction,thisOldConMusig,advantage) - totalALoss += continuousAloss - totalActionNum += 1 + advantage = tf.expand_dims(y_true[:, -1], axis=1) - loss = tf.divide(totalALoss,totalActionNum) + if self.disActSize != 0: + # while NN have discrete action output. + oldDisProbs = y_true[:, 0 : self.disOutputSize] + nowDisProbs = y_pred[:, 0 : self.disOutputSize] # [disAct1, disAct2, disAct3] + disOneHotActs = y_true[ + :, + self.disOutputSize + + self.conActSize : self.disOutputSize + + self.conActSize + + self.disOutputSize, + ] + lastDisActShape = 0 + for thisShape in self.disActShape: + thisNowDisProbs = nowDisProbs[:, lastDisActShape : lastDisActShape + thisShape] + thisOldDisProbs = oldDisProbs[:, lastDisActShape : lastDisActShape + thisShape] + thisDisOneHotActs = disOneHotActs[ + :, lastDisActShape : lastDisActShape + thisShape + ] + discreteALoss = getDiscreteALoss( + thisNowDisProbs, thisOldDisProbs, thisDisOneHotActs, thisShape, advantage + ) + lastDisActShape += thisShape + totalALoss += discreteALoss + totalActionNum += 1.0 + if self.conActSize != 0: + # while NN have continuous action output. + oldConProbs = y_true[:, self.disOutputSize : self.disOutputSize + self.conActSize] + conActions = y_true[ + :, + self.disOutputSize + + self.conActSize : self.disOutputSize + + self.conActSize + + self.conActSize, + ] + nowConMusigs = y_pred[:, self.disOutputSize :] # [musig1,musig2] + lastConAct = 0 + for conAct in range(self.conActSize): + thisNowConMusig = nowConMusigs[:, lastConAct : lastConAct + self.muSigSize] + thisOldConProb = oldConProbs[:, conAct : conAct + 1] + thisConAction = conActions[:, conAct] + continuousAloss = getContinuousALoss( + thisNowConMusig, thisConAction, thisOldConProb, advantage + ) + totalALoss += continuousAloss + totalActionNum += 1.0 + lastConAct += self.muSigSize + loss = tf.divide(totalALoss, totalActionNum) return loss + return loss - # get Action&V - def chooseAction(self, state): + # get Actions&values + def chooseAction(self, state: ndarray): """Agent choose action to take Args: - state (np.array): enviroment state + state (ndarray): enviroment state Returns: - np.array: - disAct1, - discreteAction1 - disAct2, - discreteAction2 - disAct3, - discreteAction3 - conAction, - continuousAction + np.array: + actions, + actions list,2dims like [[0],[1],[1.5]] predictResult, actor NN predict Result output """ # let actor choose action,use the normal distribution # state = np.expand_dims(state,0) - - # check state dimension is [1,statesize] - if state.ndim!=2: - state = state.reshape([1,self.stateSize]) - + + # check state dimension is [stateNum,statesize] + if state.ndim != 2: + stateNum = int(len(state) / self.stateSize) + state = state.reshape([stateNum, self.stateSize]) predictResult = self.actor(state) # get predict result [[disAct1, disAct2, disAct3, musig]] - predictResult = predictResult.numpy() - disAct1Prob = predictResult[0][0:3] - disAct2Prob = predictResult[0][3:6] - disAct3Prob = predictResult[0][6:8] - mu = predictResult[0][8] - sigma = predictResult[0][9] - if math.isnan(mu) or math.isnan(sigma): - # check mu or sigma is nan - print("mu or sigma is nan") + # print("predictResult",predictResult) + # predictResult = predictResult.numpy() + actions = [] + if self.disActSize != 0: + # while NN have discrete action output. + lastDisActShape = 0 + for shape in self.disActShape: + thisDisActProbs = predictResult[:, lastDisActShape : lastDisActShape + shape] + dist = tfp.distributions.Categorical(probs=thisDisActProbs, dtype=tf.float32) + action = int(dist.sample().numpy()[0]) + # action = np.argmax(thisDisActProbs) + actions.append(action) + lastDisActShape += shape + if self.conActSize != 0: + # while NN have continuous action output. + lastConAct = 0 + for actIndex in range(self.conActSize): + thisMu = predictResult[:, self.disOutputSize + lastConAct] + thisSig = predictResult[:, self.disOutputSize + lastConAct + 1] + if math.isnan(thisMu) or math.isnan(thisSig): + # check mu or sigma is nan + print("chooseAction:mu or sigma is nan") + thisDist = np.random.normal(loc=thisMu, scale=thisSig) + actions.append(np.clip(thisDist, -self.conActRange, self.conActRange)) + lastConAct += 2 + return actions, predictResult - disAct1 = np.argmax(disAct1Prob) # WS 0 or 1 or 2 - disAct2 = np.argmax(disAct2Prob) # AD 0 or 1 or 2 - disAct3 = np.argmax(disAct3Prob) # mouse shoot 0 or 1 - normDist = np.random.normal(loc=mu, scale=sigma) # normalDistribution - conAction = np.clip(normDist, -self.conActRange, - self.conActRange) # 在正态分布中随机get一个action - return disAct1, disAct2, disAct3, conAction, predictResult + def trainCritcActor( + self, + states: ndarray, + oldActorResult: ndarray, + actions: ndarray, + rewards: ndarray, + dones: ndarray, + nextState: ndarray, + epochs: int = None, + ): + """train critic&actor use PPO ways - def getCriticV(self, state): + Args: + states (ndarray): states + oldActorResult (ndarray): actor predict result + actions (ndarray): predicted actions include both discrete actions and continuous actions + rewards (ndarray): rewards from enviroment + dones (ndarray): dones from enviroment + nextState (ndarray): next state from enviroment + epochs (int, optional): train epochs,default to ppoConfig. Defaults to None. + + Returns: + tf.constant: criticLoss, actorLoss + """ + + if epochs == None: + epochs = self.trainEpochs + criticValues = self.getCriticV(state=states) + discountedR = self.discountReward(nextState, criticValues, dones, rewards) + advantage = self.getGAE(discountedR, criticValues) + + criticLoss = self.trainCritic(states, discountedR, epochs) + actorLoss = self.trainActor(states, oldActorResult, actions, advantage, epochs) + # print("A_Loss:", actorLoss, "C_Loss:", criticLoss) + return criticLoss, actorLoss + + def trainCritic(self, states: ndarray, discountedR: ndarray, epochs: int = None): + """critic NN trainning function + + Args: + states (ndarray): states + discountedR (ndarray): discounted rewards + epochs (int, optional): train epochs,default to ppoConfig. Defaults to None. + + Returns: + tf.constant: all critic losses + """ + if epochs == None: + epochs = self.trainEpochs + his = self.critic.fit(x=states, y=discountedR, epochs=epochs, verbose=0) + return his.history["loss"] + + def trainActor( + self, + states: ndarray, + oldActorResult: ndarray, + actions: ndarray, + advantage: ndarray, + epochs: int = None, + ): + """actor NN trainning function + + Args: + states (ndarray): states + oldActorResult (ndarray): actor predict results + actions (ndarray): acotor predict actions + advantage (ndarray): GAE advantage + epochs (int, optional): train epochs,default to ppoConfig. Defaults to None. + + Returns: + tf.constant: all actor losses + """ + # Trian Actor + # states: Buffer States + # actions: Buffer Actions + # discountedR: Discounted Rewards + # Epochs: just Epochs + if epochs == None: + epochs = self.trainEpochs + actions = np.asarray(actions, dtype=np.float32) + + disActions = actions[:, 0 : self.disActSize] + conActions = actions[:, self.disActSize :] + oldDisProbs = oldActorResult[:, 0 : self.disOutputSize] # [disAct1, disAct2, disAct3] + oldConMusigs = oldActorResult[:, self.disOutputSize :] # [musig1,musig2] + if self.disActSize != 0: + disOneHotActs = self.getOneHotActs(disActions) + if self.conActSize != 0: + # while NN have discrete6 & continuous actions output. + oldPiProbs = self.conProb(oldConMusigs[:, 0], oldConMusigs[:, 1], conActions) + # pack [oldDisProbs,oldPiProbs,conActions,advantage] as y_true + y_true = np.hstack((oldDisProbs, oldPiProbs, disOneHotActs, conActions, advantage)) + else: + # while NN have only discrete actions output. + # pack [oldDisProbs,advantage] as y_true + y_true = np.hstack((oldDisProbs, disOneHotActs, advantage)) + else: + if self.conActSize != 0: + # while NN have only continuous action output. + oldPiProbs = self.conProb(oldConMusigs[:, 0], oldConMusigs[:, 1], conActions) + # pack [oldPiProbs,conActions,advantage] as y_true + y_true = np.hstack((oldPiProbs, conActions, advantage)) + else: + print("trainActor:disActSize & conActSize error") + time.sleep(999999) + # assembly Actions history + # train start + if np.any(tf.math.is_nan(y_true)): + print("y_true got nan") + print("y_true", y_true) + his = self.actor.fit(x=states, y=y_true, epochs=epochs, verbose=0) + if np.any(tf.math.is_nan(his.history["loss"])): + print("his.history['loss'] is nan!") + print(his.history["loss"]) + return his.history["loss"] + + def saveWeights(self, score: float): + """save now NN's Weight. Use "models.save_weights" method. + Save as "tf" format "ckpt" file. + + Args: + score (float): now score + """ + actor_save_dir = ( + self.saveDir + datetime.datetime.now().strftime("%H%M%S") + "/actor/" + "actor.ckpt" + ) + critic_save_dir = ( + self.saveDir + datetime.datetime.now().strftime("%H%M%S") + "/critic/" + "critic.ckpt" + ) + self.actor.save_weights(actor_save_dir, save_format="tf") + self.critic.save_weights(critic_save_dir, save_format="tf") + # create an empty file named as score to recored score + score_dir = ( + self.saveDir + datetime.datetime.now().strftime("%H%M%S") + "/" + str(round(score)) + ) + scorefile = open(score_dir, "w") + scorefile.close() + print("Model's Weights Saved") + + def loadWeightToModels(self, loadDir: str): + """load NN Model. Use "models.load_weights()" method. + Load "tf" format "ckpt" file. + + Args: + loadDir (str): Model dir + """ + actorDir = loadDir + "/actor/" + "actor.ckpt" + criticDir = loadDir + "/critic/" + "critic.ckpt" + self.actor.load_weights(actorDir) + self.critic.load_weights(criticDir) + + print("++++++++++++++++++++++++++++++++++++") + print("++++++++++++Model Loaded++++++++++++") + print(loadDir) + print("++++++++++++++++++++++++++++++++++++") + + def getCriticV(self, state: ndarray): """get Critic predict V value Args: - state (np.array): Env state + state (ndarray): Env state Returns: tensor: retrun Critic predict result """ # if state.ndim < 2: # state = np.expand_dims(state,0) - if state.ndim!=2: - state = state.reshape([1,self.stateSize]) + if state.ndim != 2: + stateNum = int(len(state) / self.stateSize) + state = state.reshape([stateNum, self.stateSize]) return self.critic.predict(state) - def discountReward(self, nextState, rewards): + def discountReward(self, nextState: ndarray, values: ndarray, dones: ndarray, rewards: ndarray): """Discount future rewards Args: - nextState (np.array): next Env state - rewards (np.array): reward list of this episode + nextState (ndarray): next Env state + values (ndarray): critic predict values + dones (ndarray): dones from enviroment + rewards (ndarray): reward list of this episode Returns: - np.array: discounted rewards list,same shape as rewards that input + ndarray: discounted rewards list,same shape as rewards that input + """ + """ + nextV = self.getCriticV(nextState) + dones = 1 - dones + discountedRewards = [] + for i in reversed(range(len(rewards))): + nextV = rewards[i] + dones[i] * self.gamma * nextV + discountedRewards.append(nextV) + discountedRewards.reverse() # reverse + discountedRewards = np.squeeze(discountedRewards) + discountedRewards = np.expand_dims(discountedRewards, axis=1) + # discountedRewards = np.array(discountedRewards)[:, np.newaxis] + return discountedRewards + """ """ - # 降低未来的rewards nextV = self.getCriticV(nextState) discountedRewards = [] for r in rewards[::-1]: - nextV = r + self.GAMMA*nextV + nextV = r + self.gamma * nextV discountedRewards.append(nextV) - discountedRewards.reverse() # \ESREVER/ + discountedRewards.reverse() # reverse discountedRewards = np.squeeze(discountedRewards) discountedRewards = np.expand_dims(discountedRewards, axis=1) - #discountedRewards = np.array(discountedRewards)[:, np.newaxis] + # discountedRewards = np.array(discountedRewards)[:, np.newaxis] + print(discountedRewards) return discountedRewards + """ + g = 0 + discountedRewards = [] + lastValue = self.getCriticV(nextState) + values = np.append(values, lastValue, axis=0) + dones = 1 - dones + for i in reversed(range(len(rewards))): + delta = rewards[i] + self.gamma * values[i + 1] * dones[i] - values[i] + g = delta + self.gamma * self.lmbda * dones[i] * g + discountedRewards.append(g + values[i]) + discountedRewards.reverse() + return np.asarray(discountedRewards) - def conProb(self, mu, sig, x): + def getGAE(self, discountedRewards: ndarray, values: ndarray): + """compute GAE adcantage + + Args: + discountedRewards (ndarray): discounted rewards + values (ndarray): critic predict values + + Returns: + ndarray: GAE advantage + """ + advantage = discountedRewards - values + advantage = (advantage - np.mean(advantage)) / (np.std(advantage) + EPS) + return advantage + + def conProb(self, mu: ndarray, sig: ndarray, x: ndarray): """calculate probability when x in Normal distribution(mu,sigma) Args: - mu (np,array): mu - sig (np.array): sigma - x (np.array): x + mu (ndarray): mu + sig (ndarray): sigma + x (ndarray): x Returns: - np.array: probabilities + ndarray: probability """ # 获取在正态分布mu,sig下当取x值时的概率 # return shape : (length,1) @@ -309,120 +654,62 @@ class PPO(object): prob = dist.prob(x) prob = np.reshape(prob, (np.size(x), 1)) - #dist = 1./(tf.sqrt(2.*np.pi)*sig) - #prob = dist*tf.exp(-tf.square(x-mu)/(2.*tf.square(sig))) + # dist = 1./(tf.sqrt(2.*np.pi)*sig) + # prob = dist*tf.exp(-tf.square(x-mu)/(2.*tf.square(sig))) return prob - def trainCritcActor(self, states, actions, rewards, nextState, criticEpochs, actorEpochs): - # Train ActorNN and CriticNN - # states: Buffer States - # actions: Buffer Actions - # rewards: Buffer Rewards,没有Discount处理 - # nextState: 下一个单独state - # criticEpochs: just criticNN'Epochs - # acotrEpochs: just acotrNN'Epochs - discountedR = self.discountReward(nextState, rewards) - - criticMeanLoss = self.trainCritic(states, discountedR, criticEpochs) - actorMeanLoss = self.trainActor( - states, actions, discountedR, actorEpochs) - print("A_Loss:", actorMeanLoss, "C_Loss:", criticMeanLoss) - return actorMeanLoss, criticMeanLoss - - def trainCritic(self, states, discountedR, epochs): - # Trian Critic - # states: Buffer States - # discountedR: Discounted Rewards - # Epochs: just Epochs - - # IDK why this should be list...It just work... - # If discountR in np.array type it will throw 'Failed to find data adapter that can handle' - # discountedR = discountedR.tolist() - his = self.critic.fit(x=states, y=discountedR, - epochs=epochs, verbose=0) - return np.mean(his.history['loss']) - - def trainActor(self, states, actions, discountedR, epochs): - """Actor NN trainning function + def getOneHotActs(self, disActions): + """one hot action encoder Args: - states (np.array): Env states - actions (np.array): action history - discountedR (np.array): discountedR - epochs (int): epochs,how many time NN learning + disActions (ndarray): discrete actions Returns: - Average actor loss: this learning round's average actor loss + ndarray: one hot actions """ - # Trian Actor - # states: Buffer States - # actions: Buffer Actions - # discountedR: Discounted Rewards - # Epochs: just Epochs + actIndex = 0 + for thisShape in self.disActShape: + thisActs = disActions[:, actIndex] + thisOneHotAct = tf.squeeze(tf.one_hot(thisActs, thisShape)).numpy() + if actIndex == 0: + oneHotActs = thisOneHotAct + else: + oneHotActs = np.append(oneHotActs, thisOneHotAct, axis=1) + actIndex += 1 + return oneHotActs - states = np.asarray(states) - actions = np.asarray(actions, dtype=np.float32) - # predict with old Actor NN - oldActorResult = self.actor.predict(states) - - # assembly Actions history - disActions = actions[:,0:self.disActSize] - conActions = actions[:,self.disActSize:] - # assembly predictResult as old Actor's Result - oldDisProbs = oldActorResult[:,0:self.disOutputSize] # [disAct1, disAct2, disAct3] - oldConMusigs = oldActorResult[:,self.disOutputSize:] # [musig1,musig2] - oldPiProbs = self.conProb(oldConMusigs[:, 0], oldConMusigs[:, 1], conActions) - - criticV = self.critic.predict(states) - advantage = copy.deepcopy(discountedR - criticV) - - # pack [oldDisProbs,oldPiProbs,conActions,advantage] as y_true - y_true = np.hstack((oldDisProbs,oldPiProbs,conActions,advantage)) - - # train start - if np.any(tf.math.is_nan(y_true)): - print("y_true got nan") - print("oldConMusigs",oldConMusigs) - print("oldPiProbs",oldPiProbs) - print("conActions",conActions) - print("oldConMusigs",oldConMusigs) - his = self.actor.fit(x=states, y=y_true, epochs=epochs, verbose=0) - if np.any(tf.math.is_nan(his.history['loss'])): - print("his.history['loss'] is nan!") - print(his.history['loss']) - return np.mean(his.history['loss']) - - def saveWeights(self,score = None): - """save now NN's Weight. Use "models.save_weights" method. - Save as "tf" format "ckpt" file. + def getAverageEntropy(self, probs: ndarray): + """get average dis&con ACT Entropys Args: - score (int): now score - """ - actor_save_dir = self.saveDir+datetime.datetime.now().strftime("%H%M%S") + "/actor/" + "actor.ckpt" - critic_save_dir = self.saveDir+datetime.datetime.now().strftime("%H%M%S") + "/critic/" + "critic.ckpt" - self.actor.save_weights(actor_save_dir, save_format="tf") - self.critic.save_weights(critic_save_dir, save_format="tf") - if score != None: - # create an empty file named as score to recored score - score_dir = self.saveDir+datetime.datetime.now().strftime("%H%M%S") + "/" + str(round(score)) - scorefile = open(score_dir,'w') - scorefile.close() - print("Model's Weights Saved") - - def loadWeightToModels(self,loadDir): - """load NN Model. Use "models.load_weights()" method. - Load "tf" format "ckpt" file. + probs (ndarray): actor NN predict result - Args: - loadDir (string): Model dir + Returns: + float: average total entropy + list: discrete entropys + list: continuous entropys """ - actorDir = loadDir + "/actor/" + "actor.ckpt" - criticDir = loadDir + "/critic/" + "critic.ckpt" - self.actor.load_weights(actorDir) - self.critic.load_weights(criticDir) - - print("++++++++++++++++++++++++++++++++++++") - print("++++++++++++Model Loaded++++++++++++") - print(loadDir) - print("++++++++++++++++++++++++++++++++++++") + discreteEntropys = [] + continuousEntropys = [] + if self.disActSize != 0: + disProbs = probs[:, 0 : self.disOutputSize] + lastDisActIndex = 0 + for actShape in self.disActShape: + thisDisProbs = disProbs[:, lastDisActIndex : lastDisActIndex + actShape] + lastDisActIndex += actShape + discreteEntropys.append( + tf.negative( + tf.reduce_mean( + tf.math.multiply(thisDisProbs, tf.math.log(thisDisProbs + EPS)) + ) + ) + ) + if self.conActSize != 0: + conProbs = probs[:, self.disOutputSize :] + conActIndex = 0 + for i in range(self.conActSize): + thisConProbs = conProbs[:, conActIndex : conActIndex + 2] + conActIndex += 2 + continuousEntropys.append(tf.reduce_mean(thisConProbs[:, 1])) + averageEntropy = np.mean([np.mean(discreteEntropys), np.mean(continuousEntropys)]) + return averageEntropy, discreteEntropys, continuousEntropys diff --git a/Aimbot-PPO-Python/PPOBuffer.py b/Aimbot-PPO-Python/PPOBuffer.py new file mode 100644 index 0000000..049abf0 --- /dev/null +++ b/Aimbot-PPO-Python/PPOBuffer.py @@ -0,0 +1,65 @@ +import numpy as np + + +class PPOBuffer(object): + def __init__(self): + self.states = [] + self.actorProbs = [] + self.actions = [] + self.rewards = [] + self.dones = [] + print("√√√√√Buffer Initialized Success√√√√√") + + def clearBuffer(self): + self.states = [] + self.actorProbs = [] + self.actions = [] + self.rewards = [] + self.dones = [] + + def getStates(self): + return self.standDims(np.asarray(self.states)) + + def getActorProbs(self): + return self.standDims(np.asarray(self.actorProbs)) + + def getActions(self): + return self.standDims(np.asarray(self.actions)) + + def getRewards(self): + return self.standDims(np.asarray(self.rewards)) + + def getDones(self): + return self.standDims(np.asarray(self.dones)) + + def saveState(self, state): + self.states.append(state) + + def saveAction(self, action): + self.actions.append(action) + + def saveReward(self, reward): + self.rewards.append(reward) + + def standDims(self, data): + # standarlize data's dimension + if np.ndim(data) > 2: + return np.squeeze(data, axis=1) + elif np.ndim(data) < 2: + return np.expand_dims(data, axis=1) + else: + return np.asarray(data) + + def saveBuffers(self, state, actorProb, action, reward, done): + self.states.append(state) + self.actorProbs.append(actorProb) + self.actions.append(action) + self.rewards.append(reward) + self.dones.append(done) + """ + print("self.states", self.states) + print("self.actions", self.actions) + print("self.rewards", self.rewards) + print("self.dones", self.dones) + print("self.values", self.values) + """ diff --git a/Aimbot-PPO-Python/PPOConfig.py b/Aimbot-PPO-Python/PPOConfig.py new file mode 100644 index 0000000..ce3b573 --- /dev/null +++ b/Aimbot-PPO-Python/PPOConfig.py @@ -0,0 +1,15 @@ +import datetime +from typing import NamedTuple, Optional + + +class PPOConfig(NamedTuple): + NNShape: list = [256, 256, 128] + actorLR: float = 2e-3 # Actor Net Learning + criticLR: float = 2e-3 # Critic Net Learning + gamma: float = 0.99 + lmbda: float = 0.95 + clipRange: float = 0.20 + entropyWeight: float = 1e-2 + trainEpochs: int = 8 + saveDir: str = "PPO-Model/" + datetime.datetime.now().strftime("%m%d-%H%M") + "/" + loadModelDir: Optional[str] = None diff --git a/Aimbot-PPO-Python/PPOHistoryRecorder.py b/Aimbot-PPO-Python/PPOHistoryRecorder.py new file mode 100644 index 0000000..f75c446 --- /dev/null +++ b/Aimbot-PPO-Python/PPOHistoryRecorder.py @@ -0,0 +1,58 @@ +from turtle import color +import matplotlib.pyplot as plt + + +class PPOHistory(object): + def __init__(self): + self.meanRewards = [] + self.entropys = [] + self.actorLosses = [] + self.criticLosses = [] + + def saveHis(self, rewards, entropys, aLosses, cLosses): + self.meanRewards.extend([rewards]) + self.entropys.extend([entropys]) + self.actorLosses.extend(aLosses) + self.criticLosses.extend(cLosses) + + def drawHis(self): + plt.figure(figsize=(21, 13), facecolor="#011627") + ax = plt.subplot(2, 2, 1) + ax.set_facecolor("#011627") + ax.spines["top"].set_color("#c9d2df") + ax.spines["bottom"].set_color("#c9d2df") + ax.spines["left"].set_color("#c9d2df") + ax.spines["right"].set_color("#c9d2df") + ax.plot( + range(len(self.meanRewards)), self.meanRewards, color="#c9d2df", label="AverageRewards" + ) + ax.set_title("meanRewards", color="#c9d2df") + ax = plt.subplot(2, 2, 2) + ax.set_facecolor("#011627") + ax.spines["top"].set_color("#c9d2df") + ax.spines["bottom"].set_color("#c9d2df") + ax.spines["left"].set_color("#c9d2df") + ax.spines["right"].set_color("#c9d2df") + ax.plot(range(len(self.entropys)), self.entropys, color="#c9d2df", label="AverageEntropys") + ax.set_title("entropys", color="#c9d2df") + ax = plt.subplot(2, 2, 3) + ax.set_facecolor("#011627") + ax.spines["top"].set_color("#c9d2df") + ax.spines["bottom"].set_color("#c9d2df") + ax.spines["left"].set_color("#c9d2df") + ax.spines["right"].set_color("#c9d2df") + ax.plot( + range(len(self.actorLosses)), self.actorLosses, color="#c9d2df", label="actorLosses" + ) + ax.set_title("actorLosses", color="#c9d2df") + ax = plt.subplot(2, 2, 4) + ax.set_facecolor("#011627") + ax.spines["top"].set_color("#c9d2df") + ax.spines["bottom"].set_color("#c9d2df") + ax.spines["left"].set_color("#c9d2df") + ax.spines["right"].set_color("#c9d2df") + ax.plot( + range(len(self.criticLosses)), self.criticLosses, color="#c9d2df", label="criticLosses" + ) + ax.set_title("criticLosses", color="#c9d2df") + plt.show() diff --git a/Aimbot-PPO-Python/aimBotEnv.py b/Aimbot-PPO-Python/aimBotEnv.py index 99bd690..e5e1f71 100644 --- a/Aimbot-PPO-Python/aimBotEnv.py +++ b/Aimbot-PPO-Python/aimBotEnv.py @@ -1,8 +1,8 @@ -import mlagents_envs from mlagents_envs.base_env import ActionTuple from mlagents_envs.environment import UnityEnvironment import numpy as np +from numpy import ndarray class makeEnv(object): @@ -22,69 +22,71 @@ class makeEnv(object): self.BEHA_SPECS = self.env.behavior_specs self.BEHA_NAME = list(self.BEHA_SPECS)[0] self.SPEC = self.BEHA_SPECS[self.BEHA_NAME] - self.OBSERVATION_SPECS = self.SPEC.observation_specs[ - 0 - ] # observation spec + self.OBSERVATION_SPECS = self.SPEC.observation_specs[0] # observation spec self.ACTION_SPEC = self.SPEC.action_spec # action specs self.DISCRETE_SIZE = self.ACTION_SPEC.discrete_size # 　連続的な動作のSize + self.DISCRETE_SHAPE = list(self.ACTION_SPEC.discrete_branches) self.CONTINUOUS_SIZE = self.ACTION_SPEC.continuous_size # 　離散的な動作のSize - self.STATE_SIZE = ( - self.OBSERVATION_SPECS.shape[0] - self.LOAD_DIR_SIZE_IN_STATE - ) # 環境観測データ数 + self.STATE_SIZE = self.OBSERVATION_SPECS.shape[0] - self.LOAD_DIR_SIZE_IN_STATE # 環境観測データ数 print("√√√√√Enviroment Initialized Success√√√√√") def step( self, - discreteActions=None, - continuousActions=None, - behaviorName=None, - trackedAgent=None, + actions: list, + behaviorName: ndarray = None, + trackedAgent: ndarray = None, ): + """change ations list to ActionTuple then send it to enviroment + + Args: + actions (list): PPO chooseAction output action list + behaviorName (ndarray, optional): behaviorName. Defaults to None. + trackedAgent (ndarray, optional): trackedAgentID. Defaults to None. + + Returns: + ndarray: nextState, reward, done, loadDir, saveNow + """ # take action to enviroment # return mextState,reward,done + if self.DISCRETE_SIZE == 0: + # create empty discrete action + discreteActions = np.asarray([[0]]) + else: + # create discrete action from actions list + discreteActions = np.asanyarray([actions[0:self.DISCRETE_SIZE]]) + if self.CONTINUOUS_SIZE == 0: + # create empty continuous action + continuousActions = np.asanyarray([[0.0]]) + else: + # create continuous actions from actions list + continuousActions = np.asanyarray(actions[self.DISCRETE_SIZE:]) - # check if arg is include None or IS None - try: - isDisNone = discreteActions.any() is None - if discreteActions.all() is None: - print("step() Error!:discreteActions include None") - except: - isDisNone = True - try: - isConNone = continuousActions.any() is None - if continuousActions.all() is None: - print("step() Error!:continuousActions include None") - except: - isConNone = True - - if isDisNone: - # if discreteActions is enpty just give nothing[[0]] to Enviroment - discreteActions = np.array([[0]], dtype=np.int) - if isConNone: - # if continuousActions is enpty just give nothing[[0]] to Enviroment - continuousActions = np.array([[0]], dtype=np.float) if behaviorName is None: behaviorName = self.BEHA_NAME if trackedAgent is None: trackedAgent = self.TRACKED_AGENT # create actionTuple - thisActionTuple = ActionTuple( - continuous=continuousActions, discrete=discreteActions - ) + thisActionTuple = ActionTuple(continuous=continuousActions, discrete=discreteActions) # take action to env - self.env.set_actions( - behavior_name=behaviorName, action=thisActionTuple - ) + self.env.set_actions(behavior_name=behaviorName, action=thisActionTuple) self.env.step() # get nextState & reward & done after this action - nextState, reward, done, loadDir, saveNow = self.getSteps( - behaviorName, trackedAgent - ) + nextState, reward, done, loadDir, saveNow = self.getSteps(behaviorName, trackedAgent) return nextState, reward, done, loadDir, saveNow def getSteps(self, behaviorName=None, trackedAgent=None): + """get enviroment now observations. + Include State, Reward, Done, LoadDir, SaveNow + + Args: + behaviorName (_type_, optional): behaviorName. Defaults to None. + trackedAgent (_type_, optional): trackedAgent. Defaults to None. + + Returns: + ndarray: nextState, reward, done, loadDir, saveNow + """ # get nextState & reward & done if behaviorName is None: behaviorName = self.BEHA_NAME @@ -94,25 +96,17 @@ class makeEnv(object): if trackedAgent is None: trackedAgent = self.TRACKED_AGENT - if ( - trackedAgent in decisionSteps - ): # ゲーム終了していない場合、環境状態がdecision_stepsに保存される + if trackedAgent in decisionSteps: # ゲーム終了していない場合、環境状態がdecision_stepsに保存される nextState = decisionSteps[trackedAgent].obs[0] - nextState = np.reshape( - nextState, [1, self.STATE_SIZE + self.LOAD_DIR_SIZE_IN_STATE] - ) + nextState = np.reshape(nextState, [1, self.STATE_SIZE + self.LOAD_DIR_SIZE_IN_STATE]) saveNow = nextState[0][-1] loadDir = nextState[0][-3:-1] nextState = nextState[0][:-3] reward = decisionSteps[trackedAgent].reward done = False - if ( - trackedAgent in terminalSteps - ): # ゲーム終了した場合、環境状態がterminal_stepsに保存される + if trackedAgent in terminalSteps: # ゲーム終了した場合、環境状態がterminal_stepsに保存される nextState = terminalSteps[trackedAgent].obs[0] - nextState = np.reshape( - nextState, [1, self.STATE_SIZE + self.LOAD_DIR_SIZE_IN_STATE] - ) + nextState = np.reshape(nextState, [1, self.STATE_SIZE + self.LOAD_DIR_SIZE_IN_STATE]) saveNow = nextState[0][-1] loadDir = nextState[0][-3:-1] nextState = nextState[0][:-3] @@ -121,9 +115,16 @@ class makeEnv(object): return nextState, reward, done, loadDir, saveNow def reset(self): + """reset enviroment and get observations + + Returns: + ndarray: nextState, reward, done, loadDir, saveNow + """ self.env.reset() nextState, reward, done, loadDir, saveNow = self.getSteps() return nextState, reward, done, loadDir, saveNow def render(self): + """render enviroment + """ self.env.render() diff --git a/Aimbot-PPO-Python/buffer.py b/Aimbot-PPO-Python/buffer.py deleted file mode 100644 index 202cd75..0000000 --- a/Aimbot-PPO-Python/buffer.py +++ /dev/null @@ -1,29 +0,0 @@ -import numpy as np - -class buffer(object): - def __init__(self): - self.states = [] - self.actions = [] - self.rewards = [] - print("√√√√√Buffer Initialized Success√√√√√") - def clearBuffer(self): - self.states = [] - self.actions = [] - self.rewards = [] - def getStates(self): - return np.asarray(self.states) - def getActions(self): - return np.asarray(self.actions) - def getRewards(self): - return np.asarray(self.rewards) - - def saveState(self,state): - self.states.append(state) - def saveAction(self,action): - self.actions.append(action) - def saveReward(self,reward): - self.rewards.append(reward) - def saveBuffers(self,state,action,reward): - self.states.append(state) - self.actions.append(action) - self.rewards.append(reward) \ No newline at end of file diff --git a/Aimbot-PPO-Python/main.ipynb b/Aimbot-PPO-Python/main.ipynb deleted file mode 100644 index c67a65d..0000000 --- a/Aimbot-PPO-Python/main.ipynb +++ /dev/null @@ -1,356 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import aimBotEnv\n", - "import PPO\n", - "import buffer\n", - "import numpy as np\n", - "\n", - "import tensorflow as tf\n", - "import time\n", - "import datetime\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Attempts to allocate only the GPU memory needed for allocation\n", - "physical_devices = tf.config.list_physical_devices('GPU')\n", - "tf.config.experimental.set_memory_growth(physical_devices[0], True)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Env\n", - "ENV_PATH = \"./Build-CloseEnemyCut/Aimbot-PPO\"\n", - "WORKER_ID = 1\n", - "BASE_PORT = 200\n", - "\n", - "MAX_EP = 1000\n", - "EP_LENGTH = 100000\n", - "GAMMA = 0.99 # discount future reward (UP?)\n", - "EPSILON = 0.2 # clip Ratio range[1-EPSILON,1+EPSILON]\n", - "ACTOR_LR = 1e-5 # LR\n", - "CRITIC_LR = 2e-5 # LR\n", - "BATCH = 256 # learning step\n", - "ACTOR_EPOCH = 15 # epoch\n", - "CRITIC_EPOCH = 15 # epoch\n", - "ENTROPY_WHEIGHT = 0.001 # sigma's entropy in Actor loss\n", - "ACTION_INTERVAL = 1 # take action every ACTION_INTERVAL steps\n", - "\n", - "\n", - "TRAIN = True\n", - "SAVE_DIR = \"PPO-Model/\" + datetime.datetime.now().strftime(\"%m%d%H%M\") + \"/\"\n", - "LOAD_DIR = None\n", - "\n", - "CTN_ACTION_RANGE = 10\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "√√√√√Enviroment Initialized Success√√√√√\n", - "√√√√√Buffer Initialized Success√√√√√\n", - "No loadDir specified,Create a New Model\n", - "CONTINUOUS_SIZE 1\n", - "DISCRETE_SIZE 5\n", - "STATE_SIZE 30\n" - ] - } - ], - "source": [ - "# initialize enviroment & buffer class\n", - "env = aimBotEnv.makeEnv(\n", - " envPath=ENV_PATH, workerID=WORKER_ID, basePort=BASE_PORT\n", - ")\n", - "epBuffer = buffer.buffer()\n", - "\n", - "STATE_SIZE = env.STATE_SIZE\n", - "CONTINUOUS_SIZE = env.CONTINUOUS_SIZE\n", - "DISCRETE_SIZE = env.DISCRETE_SIZE\n", - "_, _, _, loadDir, _ = env.getSteps()\n", - "\n", - "# check load model or not\n", - "if np.any(loadDir == 0):\n", - " # create a new model\n", - " print(\"No loadDir specified,Create a New Model\")\n", - " LOAD_DIR = None\n", - "else:\n", - " # load model\n", - " loadDirDateSTR = str(int(loadDir[0]))\n", - " loadDirTimeSTR = str(int(loadDir[1]))\n", - " if len(loadDirDateSTR) != 8:\n", - " # fill lost 0 while converse float to string\n", - " for _ in range(8 - len(loadDirDateSTR)):\n", - " loadDirDateSTR = \"0\" + loadDirDateSTR\n", - " if len(loadDirTimeSTR) != 6:\n", - " # fill lost 0 while converse float to string\n", - " for _ in range(6 - len(loadDirTimeSTR)):\n", - " loadDirTimeSTR = \"0\" + loadDirTimeSTR\n", - " LOAD_DIR = \"PPO-Model/\" + loadDirDateSTR + \"/\" + loadDirTimeSTR\n", - " print(\"Load Model:\")\n", - " print(LOAD_DIR)\n", - "\n", - "print(\"CONTINUOUS_SIZE\", CONTINUOUS_SIZE)\n", - "print(\"DISCRETE_SIZE\", DISCRETE_SIZE)\n", - "print(\"STATE_SIZE\", STATE_SIZE)\n", - "\n", - "disActShape = [3, 3, 2]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def actToKey(disAct1,disAct2,disAct3,conAct):\n", - " kW = 0\n", - " kS = 0\n", - " kA = 0\n", - " kD = 0\n", - " mouseShoot = 0\n", - " if disAct1 == 0:\n", - " kW = 0\n", - " kS = 1\n", - " elif disAct1 == 1:\n", - " kW = 0\n", - " kS = 0\n", - " elif disAct1 == 2:\n", - " kW = 1\n", - " kS = 0\n", - " if disAct2 == 0:\n", - " kA = 0\n", - " kD = 1\n", - " elif disAct2 == 1:\n", - " kA = 0\n", - " kD = 0\n", - " elif disAct2 == 2:\n", - " kA = 1\n", - " kD = 0\n", - " mouseShoot = disAct3\n", - " return kW,kS,kA,kD,mouseShoot,conAct" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "EP 0 START\n", - "√√√√√Buffer Initialized Success√√√√√\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\UCUNI\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3474: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "c:\\Users\\UCUNI\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\numpy\\core\\_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n", - " ret = ret.dtype.type(ret / rcount)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "A_Loss: 0.4477495511372884 C_Loss: 3.155759557088216\n", - "A_Loss: 0.14549287557601928 C_Loss: 0.5123071213563283\n", - "A_Loss: 0.055241942902406055 C_Loss: 0.13002794484297434\n", - "A_Loss: 0.057325509190559384 C_Loss: 0.11068039039770762\n", - "A_Loss: 0.04376962607105573 C_Loss: 0.03923700377345085\n" - ] - } - ], - "source": [ - "bestScore = 200.0\n", - "stopTrainCounter = 0\n", - "\n", - "totalRewardHis = []\n", - "totalActorLossHis = []\n", - "totalCriticLossHis = []\n", - "epHis = []\n", - "maxTotalReward = -99999999999\n", - "\n", - "for ep in range(MAX_EP):\n", - " print(\"EP \", ep, \" START\")\n", - " # first time run game\n", - " s, _, _, _, _ = env.reset()\n", - " if ep == 0:\n", - " epBuffer = buffer.buffer()\n", - " s = s.reshape([STATE_SIZE])\n", - " agent = PPO.PPO(\n", - " stateSize=STATE_SIZE,\n", - " disActShape=disActShape,\n", - " conActSize=1,\n", - " conActRange=CTN_ACTION_RANGE,\n", - " criticLR=CRITIC_LR,\n", - " actorLR=ACTOR_LR,\n", - " gamma=GAMMA,\n", - " epsilon=EPSILON,\n", - " entropyWeight=ENTROPY_WHEIGHT,\n", - " saveDir=SAVE_DIR,\n", - " loadModelDir=LOAD_DIR,\n", - " )\n", - " step = 0\n", - " done = False\n", - " stopTrainCounter -= 1\n", - " epHis.append(ep)\n", - "\n", - " # reset total reward\n", - " epTotalReward = 0\n", - "\n", - " # Recorder list\n", - " epStepHis = []\n", - " epRewardHis = []\n", - " epActorLossHis = []\n", - " epCriticLossHis = []\n", - "\n", - " # save weight immediately?\n", - " saveNow = 0\n", - "\n", - " while not done:\n", - " step += 1\n", - " if (\n", - " step % ACTION_INTERVAL == 0\n", - " ): # take action every ACTION_INTERVAL steps\n", - " epStepHis.append(step)\n", - " (\n", - " disAct1,\n", - " disAct2,\n", - " disAct3,\n", - " conAct,\n", - " predictResult,\n", - " ) = agent.chooseAction(s)\n", - " kW, kS, kA, kD, mouseShoot, mouseMove = actToKey(\n", - " disAct1, disAct2, disAct3, conAct\n", - " )\n", - "\n", - " nextState, thisReward, done, _, saveNow = env.step(\n", - " discreteActions=np.array([[kW, kS, kA, kD, mouseShoot]]),\n", - " continuousActions=np.array([[mouseMove]]),\n", - " )\n", - "\n", - " epTotalReward += thisReward\n", - " epBuffer.saveBuffers(\n", - " s, [disAct1, disAct2, disAct3, conAct], thisReward\n", - " )\n", - " else:\n", - " disActs = np.array([[0, 0, 0, 0, 0]])\n", - " conActs = np.array([[0]])\n", - "\n", - " nextState, thisReward, done, _, saveNow = env.step(\n", - " discreteActions=disActs, continuousActions=conActs\n", - " )\n", - " epTotalReward += thisReward\n", - " nextState = nextState.reshape([STATE_SIZE])\n", - " s = nextState\n", - "\n", - " if done:\n", - " print(\"EP OVER!\")\n", - " if saveNow != 0:\n", - " print(\"SAVENOW!\")\n", - " saveNow = 0\n", - " agent.saveWeights()\n", - " # update PPO after Batch step or GameOver\n", - " if (step + 1) % BATCH == 0 or done:\n", - " bs = epBuffer.getStates()\n", - " ba = epBuffer.getActions()\n", - " br = epBuffer.getRewards()\n", - " epBuffer.clearBuffer()\n", - " if TRAIN:\n", - " epActorLoss, epCriticLoss = agent.trainCritcActor(\n", - " bs, ba, br, s, CRITIC_EPOCH, ACTOR_EPOCH\n", - " )\n", - " epActorLossHis.append(epActorLoss)\n", - " epCriticLossHis.append(epCriticLoss)\n", - " # update History Recorder\n", - " totalActorLossHis.append(np.mean(epActorLossHis))\n", - " totalCriticLossHis.append(np.mean(epCriticLossHis))\n", - " totalRewardHis.append(epTotalReward)\n", - "\n", - " if epTotalReward > maxTotalReward and epTotalReward != 0:\n", - " maxTotalReward = epTotalReward\n", - " agent.saveWeights(epTotalReward)\n", - " print(\"New Record! Save NN\", epTotalReward)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "aaa = 0\n", - "aaa = 1\n", - "aaa = 2\n", - "aaa = 3\n", - "aaa = 4\n", - "aaa = 5\n", - "aaa = 6\n", - "aaa = 7\n", - "aaa = 8\n", - "aaa = 9\n" - ] - } - ], - "source": [ - "aaa = 0\n", - "while aaa<10:\n", - " print(\"aaa = \",aaa)\n", - " aaa+=1" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "86e2db13b09bd6be22cb599ea60c1572b9ef36ebeaa27a4c8e961d6df315ac32" - }, - "kernelspec": { - "display_name": "Python 3.9.7 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Aimbot-PPO-Python/main.py b/Aimbot-PPO-Python/main.py deleted file mode 100644 index fd5a5cb..0000000 --- a/Aimbot-PPO-Python/main.py +++ /dev/null @@ -1,25 +0,0 @@ -import aimBotEnv -import PPO - -ENV_PATH = './Build/Aimbot-PPO' -WORKER_ID = 100 - -MAX_EP = 1000 -EP_LENGTH = 400 -GAMMA = 0.99 # discount future reward (UP?) -EPSILON = 0.2 # clip Ratio range[1-EPSILON,1+EPSILON] -ACTOR_LR = 1e-5 # LR -CRITIC_LR = 2e-5 # LR -BATCH = 32 # learning step -ACTOR_EPOCH = 10 # epoch -CRITIC_EPOCH = 10 # epoch -ENTROPY_WHEIGHT = 0.01 # sigma's entropy in Actor loss -ACTION_INTERVAL = 1 # take action every ACTION_INTERVAL steps -TRAIN = True - -env = aimBotEnv.makeEnv(envPath = ENV_PATH,workerID = WORKER_ID) -STATE_SIZE = env.STATE_SIZE -CONTINUOUS_SIZE = env.CONTINUOUS_SIZE -DISCRETE_SIZE = env.DISCRETE_SIZE - -CTN_ACTION_RANGE = 2 \ No newline at end of file diff --git a/Aimbot-PPO-Python/testarea.ipynb b/Aimbot-PPO-Python/testarea.ipynb index 5539d4d..2d71fe7 100644 --- a/Aimbot-PPO-Python/testarea.ipynb +++ b/Aimbot-PPO-Python/testarea.ipynb @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -182,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -205,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -240,16 +240,16 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "True" + "array([[0.]])" ] }, - "execution_count": 20, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -259,28 +259,119 @@ "\n", "a = np.array([10, 20, 30, 0])\n", "\n", - "np.any(a == 0)\n" + "np.asarray([[0.]])\n" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4" + "1.5" ] }, - "execution_count": 1, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "asd = \"adsf\"\n", - "len(asd)" + "import numpy as np\n", + "\n", + "asd = [1,2,3,np.array([0.5]),np.array([0.5])]\n", + "\n", + "asd[3:]\n", + "len(asd)\n", + "\n", + "np.mean([1,2])" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0\n", + "0.0\n" + ] + } + ], + "source": [ + "import time\n", + "import pyautogui as pag\n", + "\n", + "from pynput.mouse import Button, Controller\n", + "\n", + "w = pag.size().width\n", + "h = pag.size().height\n", + "mouse = Controller()\n", + "\n", + "nowt = time.time()\n", + "\n", + "middletime = time.time() - nowt\n", + "print(middletime)\n", + "# print(nowPos-(w/2))\n", + "\n", + "print(time.time() - middletime - nowt)\n", + "while True:\n", + " x,_ = mouse.position\n", + " #print(mouse.press)\n", + " #print(mouse.position)\n", + " \n", + " mouse.position = (w / 2, h / 2)\n", + " time.sleep(1/60)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "import pyautogui as pag\n", + "\n", + "import mouse\n", + "\n", + "w = pag.size().width\n", + "h = pag.size().height\n", + "\n", + "nowt = time.time()\n", + "\n", + "middletime = time.time() - nowt\n", + "print(middletime)\n", + "# print(nowPos-(w/2))\n", + "\n", + "print(time.time() - middletime - nowt)\n", + "while True:\n", + " x = mouse.get_position()\n", + " print(x)\n", + " #print(mouse.position)\n", + " \n", + " mouse.move(w / 2, h / 2)\n", + " time.sleep(1/60)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import keyboard\n", + "\n", + "while True:\n", + " if keyboard.is_pressed(\"w\"):\n", + " print(\"w\")\n", + " elif keyboard.is_pressed(\"s\"):\n", + " print(\"s\")" ] } ],