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7497ffcb0f
Aimbot-PPO/Aimbot-PPO-Python/Pytorch/testarea.ipynb
Koha9 7497ffcb0f Parallel Environment Discrete PPO finish 
Parallel Environment Discrete PPO finish. Runnable.
2022-10-30 04:13:14 +09:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Action, 1 continuous ctrl 2.1\n",
"Action, 0 continuous ctrl -1.1\n"
]
}
],
"source": [
"import gym\n",
"from gym.spaces import Dict, Discrete, Box, Tuple\n",
"import numpy as np\n",
"\n",
"\n",
"class SampleGym(gym.Env):\n",
" def __init__(self, config={}):\n",
" self.config = config\n",
" self.action_space = Tuple((Discrete(2), Box(-10, 10, (2,))))\n",
" self.observation_space = Box(-10, 10, (2, 2))\n",
" self.p_done = config.get(\"p_done\", 0.1)\n",
"\n",
" def reset(self):\n",
" return self.observation_space.sample()\n",
"\n",
" def step(self, action):\n",
" chosen_action = action[0]\n",
" cnt_control = action[1][chosen_action]\n",
"\n",
" if chosen_action == 0:\n",
" reward = cnt_control\n",
" else:\n",
" reward = -cnt_control - 1\n",
"\n",
" print(f\"Action, {chosen_action} continuous ctrl {cnt_control}\")\n",
" return (\n",
" self.observation_space.sample(),\n",
" reward,\n",
" bool(np.random.choice([True, False], p=[self.p_done, 1.0 - self.p_done])),\n",
" {},\n",
" )\n",
"\n",
"\n",
"if __name__ == \"__main__\":\n",
" env = SampleGym()\n",
" env.reset()\n",
" env.step((1, [-1, 2.1])) # should say use action 1 with 2.1\n",
" env.step((0, [-1.1, 2.1])) # should say use action 0 with -1.1"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from mlagents_envs.environment import UnityEnvironment\n",
"from gym_unity.envs import UnityToGymWrapper\n",
"import numpy as np\n",
"\n",
"ENV_PATH = \"../Build-ParallelEnv/Aimbot-ParallelEnv\"\n",
"WORKER_ID = 1\n",
"BASE_PORT = 2002\n",
"\n",
"env = UnityEnvironment(\n",
" file_name=ENV_PATH,\n",
" seed=1,\n",
" side_channels=[],\n",
" worker_id=WORKER_ID,\n",
" base_port=BASE_PORT,\n",
")\n",
"\n",
"trackedAgent = 0\n",
"env.reset()\n",
"BEHA_SPECS = env.behavior_specs\n",
"BEHA_NAME = list(BEHA_SPECS)[0]\n",
"SPEC = BEHA_SPECS[BEHA_NAME]\n",
"print(SPEC)\n",
"\n",
"decisionSteps, terminalSteps = env.get_steps(BEHA_NAME)\n",
"\n",
"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\n",
" nextState = decisionSteps[trackedAgent].obs[0]\n",
" reward = decisionSteps[trackedAgent].reward\n",
" done = False\n",
"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\n",
" nextState = terminalSteps[trackedAgent].obs[0]\n",
" reward = terminalSteps[trackedAgent].reward\n",
" done = True\n",
"print(decisionSteps.agent_id)\n",
"print(terminalSteps)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"decisionSteps.agent_id [1 2 5 7]\n",
"decisionSteps.agent_id_to_index {1: 0, 2: 1, 5: 2, 7: 3}\n",
"decisionSteps.reward [0. 0. 0. 0.]\n",
"decisionSteps.action_mask [array([[False, False, False],\n",
" [False, False, False],\n",
" [False, False, False],\n",
" [False, False, False]]), array([[False, False, False],\n",
" [False, False, False],\n",
" [False, False, False],\n",
" [False, False, False]]), array([[False, False],\n",
" [False, False],\n",
" [False, False],\n",
" [False, False]])]\n",
"decisionSteps.obs [ 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. -15.994009 1. -26.322788 1.\n",
" 1. 1. 1. 1. 1. 2.\n",
" 1. 1. 1. 1. 1. 1.\n",
" 1. 1.3519633 1.6946528 2.3051548 3.673389 9.067246\n",
" 17.521473 21.727095 22.753294 24.167128 25.905216 18.35725\n",
" 21.02278 21.053417 0. ]\n"
]
},
{
"data": {
"text/plain": [
"'decisionSteps.obs [array([[-15.994009 , 1. , -26.322788 , 1. , 1. ,\\n 1. , 1. , 1. , 1. , 2. ,\\n 1. , 1. , 1. , 1. , 1. ,\\n 1. , 1. , 1.3519633, 1.6946528, 2.3051548,\\n 3.673389 , 9.067246 , 17.521473 , 21.727095 , 22.753294 ,\\n 24.167128 , 25.905216 , 18.35725 , 21.02278 , 21.053417 ,\\n 0. ],\\n [ -1.8809433, 1. , -25.66834 , 1. , 2. ,\\n 1. , 1. , 1. , 1. , 1. ,\\n 1. , 1. , 1. , 1. , 1. ,\\n 1. , 1. , 16.768637 , 23.414627 , 22.04486 ,\\n 21.050663 , 20.486784 , 20.486784 , 21.050665 , 15.049731 ,\\n 11.578419 , 9.695194 , 20.398016 , 20.368341 , 20.398016 ,\\n...\\n 20.551746 , 20.00118 , 20.001116 , 20.551594 , 21.5222 ,\\n 17.707508 , 14.86889 , 19.914494 , 19.885508 , 19.914463 ,\\n 0. ]], dtype=float32)]'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"decisionSteps.agent_id\",decisionSteps.agent_id)\n",
"# decisionSteps.agent_id [1 2 5 7]\n",
"print(\"decisionSteps.agent_id_to_index\",decisionSteps.agent_id_to_index)\n",
"# decisionSteps.agent_id_to_index {1: 0, 2: 1, 5: 2, 7: 3}\n",
"print(\"decisionSteps.reward\",decisionSteps.reward)\n",
"# decisionSteps.reward [0. 0. 0. 0.]\n",
"print(\"decisionSteps.action_mask\",decisionSteps.action_mask)\n",
"'''\n",
"decisionSteps.action_mask [array([[False, False, False],\n",
" [False, False, False],\n",
" [False, False, False],\n",
" [False, False, False]]), array([[False, False, False],\n",
" [False, False, False],\n",
" [False, False, False],\n",
" [False, False, False]]), array([[False, False],\n",
" [False, False],\n",
" [False, False],\n",
" [False, False]])]\n",
"'''\n",
"print(\"decisionSteps.obs\", decisionSteps.obs[0][0])\n",
"'''decisionSteps.obs [array([[-15.994009 , 1. , -26.322788 , 1. , 1. ,\n",
" 1. , 1. , 1. , 1. , 2. ,\n",
" 1. , 1. , 1. , 1. , 1. ,\n",
" 1. , 1. , 1.3519633, 1.6946528, 2.3051548,\n",
" 3.673389 , 9.067246 , 17.521473 , 21.727095 , 22.753294 ,\n",
" 24.167128 , 25.905216 , 18.35725 , 21.02278 , 21.053417 ,\n",
" 0. ],\n",
" [ -1.8809433, 1. , -25.66834 , 1. , 2. ,\n",
" 1. , 1. , 1. , 1. , 1. ,\n",
" 1. , 1. , 1. , 1. , 1. ,\n",
" 1. , 1. , 16.768637 , 23.414627 , 22.04486 ,\n",
" 21.050663 , 20.486784 , 20.486784 , 21.050665 , 15.049731 ,\n",
" 11.578419 , 9.695194 , 20.398016 , 20.368341 , 20.398016 ,\n",
"...\n",
" 20.551746 , 20.00118 , 20.001116 , 20.551594 , 21.5222 ,\n",
" 17.707508 , 14.86889 , 19.914494 , 19.885508 , 19.914463 ,\n",
" 0. ]], dtype=float32)]'''\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from AimbotEnv import Aimbot\n",
"\n",
"ENV_PATH = \"../Build-ParallelEnv/Aimbot-ParallelEnv\"\n",
"WORKER_ID = 1\n",
"BASE_PORT = 2002\n",
"\n",
"env = Aimbot(envPath=ENV_PATH,workerID= WORKER_ID,basePort= BASE_PORT)\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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" 1. , 1. , 1. , 1. , 1. ,\n",
" 1. , 1. , 1. , 1. , 1. ,\n",
" 1. , 1. , 1. , 20.33171 , 22.859762 ,\n",
" 21.522427 , 20.551746 , 20.00118 , 20.001116 , 20.551594 ,\n",
" 21.5222 , 17.707508 , 14.86889 , 19.914494 , 19.885508 ,\n",
" 19.914463 , 0. , -4.9038773, 1. , -25.185507 ,\n",
" 1. , 2. , 1. , 2. , 1. ,\n",
" 1. , 1. , 1. , 2. , 1. ,\n",
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" 20.551619 , 7.135157 , 17.707582 , 14.868943 , 19.914528 ,\n",
" 19.88554 , 19.914494 , 0. ]], dtype=float32),\n",
" [[-0.05], [-0.05], [-0.05], [-0.05]],\n",
" [[False], [False], [False], [False]])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"env.unity_observation_shape\n",
"(128, 4) + env.unity_observation_shape\n",
"env.reset()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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" [[False], [False], [False], [False]])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"actions = np.zeros_like(np.arange(16).reshape(4, 4))\n",
"print(actions)\n",
"env.step(actions)"
]
}
],
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}
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