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Author SHA1 Message Date
573b09a920 Argument説明 2024-03-02 17:36:33 +09:00
9d9524429c 整理无用变量,对环境3.6进行适配 2024-01-24 17:07:45 +09:00
5aa7e0936a 参数Critic_coef修改 2023-11-23 15:29:13 +09:00
3bc5c30fd3 添加对Save_in_next_Trainning的SideChannel支持 2023-10-15 06:05:59 +09:00
2741d6d51a 将Tensor改为tensor
Tensor与tensor的问题,规范化tensor使用。
2023-08-08 20:47:56 +09:00
9432eaa76e 完全分离NN
使用根据Target完全分离的NN进行预测和训练。
修改NN构造,修改预测预测算法以配合Full Multi NN
2023-08-04 05:13:32 +09:00
8 changed files with 266 additions and 116 deletions

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@ -11,6 +11,7 @@ from mlagents_envs.side_channel.side_channel import (
IncomingMessage, IncomingMessage,
OutgoingMessage, OutgoingMessage,
) )
from arguments import set_save_model
class Aimbot(gym.Env): class Aimbot(gym.Env):
@ -176,18 +177,21 @@ class AimbotSideChannel(SideChannel):
"Warning|Message1|Message2|Message3" or "Warning|Message1|Message2|Message3" or
"Error|Message1|Message2|Message3" "Error|Message1|Message2|Message3"
""" """
this_message = msg.read_string() this_message_Original = msg.read_string()
this_result = this_message.split("|") this_message = this_message_Original.split("|")
print(this_result) print(this_message)
if this_result[0] == "Warning": if this_message[0] == "Warning":
if this_result[1] == "Result": if this_message[1] == "Result":
airecorder.total_rounds[this_result[2]] += 1 airecorder.total_rounds[this_message[2]] += 1
if this_result[3] == "Win": if this_message[3] == "Win":
airecorder.win_rounds[this_result[2]] += 1 airecorder.win_rounds[this_message[2]] += 1
# print(TotalRounds) # print(TotalRounds)
# print(WinRounds) # print(WinRounds)
elif this_result[0] == "Error": if this_message[1] == "Command":
print(this_message) set_save_model(True)
print("Command: " + this_message_Original)
elif this_message[0] == "Error":
print(this_message_Original)
# # while Message type is Warning # # while Message type is Warning
# if(thisResult[0] == "Warning"): # if(thisResult[0] == "Warning"):
# # while Message1 is result means one game is over # # while Message1 is result means one game is over

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@ -181,30 +181,84 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"array([[0., 0., 0., 0.],\n", "3"
" [0., 0., 0., 0.],\n",
" [0., 0., 0., 0.],\n",
" [0., 0., 0., 0.],\n",
" [0., 0., 0., 0.],\n",
" [0., 0., 0., 0.],\n",
" [0., 0., 0., 0.],\n",
" [0., 0., 0., 0.]])"
] ]
}, },
"execution_count": 6, "execution_count": 4,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"import numpy as np\n", "y=\"a;b;c\"\n",
"np.zeros((8, 4))" "len(y.split(\";\"))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2]\n"
]
}
],
"source": [
"a = np.array([1,2,3,4])\n",
"print(a[[False,True,False,False]])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{1, 2, 3, 4}"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = {1,2,3}\n",
"a.add(4)\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([3, 4])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([[1,3],[2,4]])\n",
"a.max(axis=1)\n"
] ]
} }
], ],

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@ -4,6 +4,7 @@ import random
import uuid import uuid
import torch import torch
import atexit import atexit
import os
from aimbotEnv import Aimbot from aimbotEnv import Aimbot
from aimbotEnv import AimbotSideChannel from aimbotEnv import AimbotSideChannel
@ -12,13 +13,14 @@ from airecorder import WandbRecorder
from aimemory import PPOMem from aimemory import PPOMem
from aimemory import Targets from aimemory import Targets
from arguments import parse_args from arguments import parse_args
from arguments import set_save_model, is_save_model
import torch.optim as optim import torch.optim as optim
# side channel uuid # side channel uuid
SIDE_CHANNEL_UUID = uuid.UUID("8bbfb62a-99b4-457c-879d-b78b69066b5e") SIDE_CHANNEL_UUID = uuid.UUID("8bbfb62a-99b4-457c-879d-b78b69066b5e")
# tensorboard names # tensorboard names
GAME_NAME = "Aimbot_Hybrid_V3" GAME_NAME = "Aimbot_Hybrid_Full_MNN_MultiLevel"
GAME_TYPE = "Mix_Verification" GAME_TYPE = "GotoOnly-3.6-Level0123-newModel-Onehot"
if __name__ == "__main__": if __name__ == "__main__":
args = parse_args() args = parse_args()
@ -47,9 +49,8 @@ if __name__ == "__main__":
# freeze # freeze
if args.freeze_viewnet: if args.freeze_viewnet:
# freeze the view network # freeze the view network
for p in agent.viewNetwork.parameters(): print("FREEZE VIEW NETWORK is not compatible with Full MNN!")
p.requires_grad = False raise NotImplementedError
print("VIEW NETWORK FREEZE")
print("Load Agent", args.load_dir) print("Load Agent", args.load_dir)
print(agent.eval()) print(agent.eval())
# optimizer # optimizer
@ -58,16 +59,6 @@ if __name__ == "__main__":
run_name = f"{GAME_TYPE}_{args.seed}_{int(time.time())}" run_name = f"{GAME_TYPE}_{args.seed}_{int(time.time())}"
wdb_recorder = WandbRecorder(GAME_NAME, GAME_TYPE, run_name, args) wdb_recorder = WandbRecorder(GAME_NAME, GAME_TYPE, run_name, args)
@atexit.register
def save_model():
# close env
env.close()
if args.save_model:
# save model while exit
save_dir = "../PPO-Model/" + run_name + "_last.pt"
torch.save(agent, save_dir)
print("save model to " + save_dir)
# start the game # start the game
total_update_step = args.target_num * args.total_timesteps // args.datasetSize total_update_step = args.target_num * args.total_timesteps // args.datasetSize
target_steps = [0 for i in range(args.target_num)] target_steps = [0 for i in range(args.target_num)]
@ -112,7 +103,7 @@ if __name__ == "__main__":
with torch.no_grad(): with torch.no_grad():
# predict actions # predict actions
action, dis_logprob, _, con_logprob, _, value = agent.get_actions_value( action, dis_logprob, _, con_logprob, _, value = agent.get_actions_value(
torch.Tensor(state).to(device) torch.tensor(state,dtype=torch.float32).to(device)
) )
value = value.flatten() value = value.flatten()
@ -223,11 +214,16 @@ if __name__ == "__main__":
) )
# print cost time as seconds # print cost time as seconds
print("cost time:", time.time() - start_time) print("cost time:", time.time() - start_time)
# New Record! # New Record! or save model
if TotalRewardMean > best_reward and args.save_model: if ((is_save_model() or TotalRewardMean > best_reward) and args.save_model):
best_reward = target_reward_mean # check saveDir is exist
saveDir = "../PPO-Model/" + run_name + "_" + str(TotalRewardMean) + ".pt" saveDir = "../PPO-Model/" + run_name + "/"
torch.save(agent, saveDir) if not os.path.isdir(saveDir):
os.mkdir(saveDir)
best_reward = TotalRewardMean
torch.save(agent, saveDir + str(TotalRewardMean) + ".pt")
print("Model Saved!")
set_save_model(False)
else: else:
# train mode off # train mode off
mean_reward_list = [] # for WANDB mean_reward_list = [] # for WANDB
@ -250,7 +246,10 @@ if __name__ == "__main__":
TotalRewardMean = np.mean(mean_reward_list) TotalRewardMean = np.mean(mean_reward_list)
wdb_recorder.writer.add_scalar("GlobalCharts/TotalRewardMean", TotalRewardMean, total_steps) wdb_recorder.writer.add_scalar("GlobalCharts/TotalRewardMean", TotalRewardMean, total_steps)
saveDir = "../PPO-Model/" + run_name + "_last.pt" saveDir = "../PPO-Model/" + run_name + "/"
torch.save(agent, saveDir) if not os.path.isdir(saveDir):
os.mkdir(saveDir)
best_reward = target_reward_mean
torch.save(agent, saveDir + "_last.pt")
env.close() env.close()
wdb_recorder.writer.close() wdb_recorder.writer.close()

View File

@ -58,10 +58,11 @@ class PPOMem:
# print("Win! Broadcast reward!",rewardBF[-1]) # print("Win! Broadcast reward!",rewardBF[-1])
print(sum(thisRewardBF) / len(thisRewardBF)) print(sum(thisRewardBF) / len(thisRewardBF))
thisRewardBF[-1] = rewardBF[-1] - self.base_win_reward thisRewardBF[-1] = rewardBF[-1] - self.base_win_reward
# broadcast result reward, increase all reward in this round by remainTime * self.result_broadcast_ratio
thisRewardBF = (np.asarray(thisRewardBF) + (remainTime * self.result_broadcast_ratio)).tolist() thisRewardBF = (np.asarray(thisRewardBF) + (remainTime * self.result_broadcast_ratio)).tolist()
else: else:
print("!!!!!DIDNT GET RESULT REWARD!!!!!!", rewardBF[-1]) print("!!!!!DIDNT GET RESULT REWARD!!!!!!", rewardBF[-1])
return torch.Tensor(thisRewardBF).to(self.device) return torch.tensor(thisRewardBF,dtype=torch.float32).to(self.device)
def save_memories( def save_memories(
self, self,
@ -88,7 +89,7 @@ class PPOMem:
self.dones_bf[i].append(done[i]) self.dones_bf[i].append(done[i])
self.values_bf[i].append(value_cpu[i]) self.values_bf[i].append(value_cpu[i])
if now_step % self.decision_period == 0: if now_step % self.decision_period == 0:
# on decision period, add last skiped round's reward # on decision period, add last skiped round's reward, only affact in decision_period != 1
self.rewards_bf[i].append(reward[i] + last_reward[i]) self.rewards_bf[i].append(reward[i] + last_reward[i])
else: else:
# not on decision period, only add this round's reward # not on decision period, only add this round's reward
@ -101,10 +102,10 @@ class PPOMem:
thisRewardsTensor = self.broad_cast_end_reward(self.rewards_bf[i], remainTime) thisRewardsTensor = self.broad_cast_end_reward(self.rewards_bf[i], remainTime)
adv, rt = agent.gae( adv, rt = agent.gae(
rewards=thisRewardsTensor, rewards=thisRewardsTensor,
dones=torch.Tensor(self.dones_bf[i]).to(self.device), dones=torch.tensor(self.dones_bf[i],dtype=torch.float32).to(self.device),
values=torch.tensor(self.values_bf[i]).to(self.device), values=torch.tensor(self.values_bf[i]).to(self.device),
next_obs=torch.tensor(next_state[i]).to(self.device).unsqueeze(0), next_obs=torch.tensor(next_state[i]).to(self.device).unsqueeze(0),
next_done=torch.Tensor([next_done[i]]).to(self.device), next_done=torch.tensor([next_done[i]],dtype=torch.float32).to(self.device),
) )
# send memories to training datasets # send memories to training datasets
self.obs[roundTargetType] = torch.cat((self.obs[roundTargetType], torch.tensor(np.array(self.ob_bf[i])).to(self.device)), 0) self.obs[roundTargetType] = torch.cat((self.obs[roundTargetType], torch.tensor(np.array(self.ob_bf[i])).to(self.device)), 0)

View File

@ -0,0 +1,56 @@
本项目使用以下命令行参数来配置运行环境和模型训练参数:
- `--seed <int>`:实验的随机种子。默认值为`9331`。
- `--path <str>`:环境路径。默认值为`"./Build/3.6/Aimbot-ParallelEnv"`。
- `--workerID <int>`Unity worker ID。默认值为`1`。
- `--baseport <int>`用于连接Unity环境的端口。默认值为`500`。
- `--lr <float>`:优化器的默认学习率。默认值为`5e-5`。
- `--cuda`如果启用将默认使用cuda。可以通过传入`true`或`false`来开启或关闭。
- `--total-timesteps <int>`:实验的总时间步数。默认值为`3150000`。
### 模型参数
- `--train`:是否训练模型。默认启用。
- `--freeze-viewnet`:是否冻结视图网络(raycast)。默认为`False`。
- `--datasetSize <int>`:训练数据集的大小,当数据集收集足够的数据时开始训练。默认值为`6000`。
- `--minibatchSize <int>`minibatch大小。默认值为`512`。
- `--epochs <int>`更新策略的K次迭代。默认值为`3`。
- `--annealLR`:是否对策略和价值网络进行学习率退火。默认为`True`。
- `--wandb-track`是否在wandb上跟踪。默认为`False`。
- `--save-model`:是否保存模型。默认为`False`。
- `--wandb-entity <str>`wandb项目的实体。默认值为`"koha9"`。
- `--load-dir <str>`:模型加载目录。默认值为`None`。
- `--decision-period <int>`Timestep之间的动作执行间隔。默认值为`1`。
- `--result-broadcast-ratio <float>`当赢得回合时对结果的reward进行broadcast的比例默认值为`1/30`。
- `--target-lr <float>`:下调学习率的目标值。默认值为`1e-6`。
### 损失函数参数
- `--policy-coef <float>`:策略损失的系数。默认值为`[0.8, 0.8, 0.8, 0.8]`。
- `--entropy-coef <float>`:熵损失的系数。默认值为`[0.05, 0.05, 0.05, 0.05]`。
- `--critic-coef <float>`:评论家损失的系数。默认值为`[1.0, 1.0, 1.0, 1.0]`。
- `--loss-coef <float>`:总损失的系数。默认值为`[1.0, 1.0, 1.0, 1.0]`。
### GAE损失参数
- `--gae`是否使用GAE进行优势计算。默认启用。
- `--norm-adv`:是否标准化优势。默认为`False`。
- `--gamma <float>`折扣因子gamma。默认值为`0.999`。
- `--gaeLambda <float>`GAE的lambda值。默认值为`0.95`。
- `--clip-coef <float>`:替代裁剪系数。默认值为`0.11`。
- `--clip-vloss`:是否使用论文中的裁剪价值函数损失。默认启用。
- `--max-grad-norm <float>`:梯度裁剪的最大范数。默认值为`0.5`。
### 环境参数
- `--target-num <int>`:目标种类数量。默认值为`4`。
- `--env-timelimit <int>`:每轮的时间限制。默认值为`30`。
- `--base-win-reward <int>`:赢得回合的基础奖励。默认值为`999`。
- `--base-lose-reward <int>`:输掉回合的基础奖励。默认值为`-999`。
- `--target-state-size <int>`target状态的大小。默认值为`6`。
- `--time-state-size <int>`:游戏剩余时间状态的大小。默认值为`1`。
- `--gun-state-size <int>`:枪状态的大小。默认值为`1`。
- `--my-state-size <int>`:我的状态大小。默认值为`4`。
- `--total-target-size <int>`总target状态的大小。默认值为`12`。

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@ -0,0 +1,52 @@
- `--seed <int>`実験の乱数Seed。デフォルト値は`9331`。
- `--path <str>`:環境パス。デフォルト値は`"./Build/3.6/Aimbot-ParallelEnv"`。
- `--workerID <int>`Unity Worker ID。デフォルト値は`1`。
- `--baseport <int>`Unity環境への接続用Port。デフォルト値は`500`。
- `--lr <float>`Optimizerのデフォルト学習率。デフォルト値は`5e-5`。
- `--cuda`有効にすると、デフォルトでcudaを使用します。`true`または`false`を渡すことで有効/無効を切り替えられます。
- `--total-timesteps <int>`:実験の合計タイムステップ数。デフォルト値は`3150000`。
### モデルパラメータ
- `--train`:モデルを訓練するかどうか。デフォルトで有効。
- `--freeze-viewnet`:ビューネットワーク(raycast)をfreezeする。デフォルトは`False`。
- `--datasetSize <int>`:訓練データセットのサイズ。データセットが十分なデータを集めたら訓練を開始する。デフォルト値は`6000`。
- `--minibatchSize <int>`minibatchのサイズ。デフォルト値は`512`。
- `--epochs <int>`epochs。デフォルト値は`3`。
- `--annealLR`:ポリシーとバリューネットワークの学習率を退火するかどうか。デフォルトは`True`。
- `--wandb-track`wandbでトラッキングするかどうか。デフォルトは`False`。
- `--save-model`:モデルを保存するかどうか。デフォルトは`False`。
- `--wandb-entity <str>`wandbプロジェクトのエンティティ。デフォルト値は`"koha9"`。
- `--load-dir <str>`:モデルのロードディレクトリ。デフォルト値は`None`。
- `--decision-period <int>`:実際動作を実行する時のタイムステップの間隔。デフォルト値は`1`。
- `--result-broadcast-ratio <float>`ラウンドに勝った場合の報酬のbroadcast ratio、デフォルト値は`1/30`。
- `--target-lr <float>`:学習率を下げる時の目標値。デフォルト値は`1e-6`。
### 損失関数パラメータ
- `--policy-coef <float>`policy損失の係数。デフォルト値は`[0.8, 0.8, 0.8, 0.8]`。
- `--entropy-coef <float>`entropy損失の係数。デフォルト値は`[0.05, 0.05, 0.05, 0.05]`。
- `--critic-coef <float>`critic損失の係数。デフォルト値は`[1.0, 1.0, 1.0, 1.0]`。
- `--loss-coef <float>`:全体の損失の係数。デフォルト値は`[1.0, 1.0, 1.0, 1.0]`。
### GAE損失パラメータ
- `--gae`GAEを使用してアドバンテージを計算するかどうか。デフォルトで有効。
- `--norm-adv`:アドバンテージを正規化するかどうか。デフォルトは`False`。
- `--gamma <float>`割引因子gamma。デフォルト値は`0.999`。
- `--gaeLambda <float>`GAEのlambda値。デフォルト値は`0.95`。
- `--clip-coef <float>`:代替クリッピング係数。デフォルト値は`0.11`。
- `--clip-vloss`:論文で述べられている価値関数の損失のクリッピングを使用するかどうか。デフォルトで有効。
- `--max-grad-norm <float>`:勾配のクリッピングの最大ノルム。デフォルト値は`0.5`。
### 環境パラメータ
- `--target-num <int>`Targetの種類数。デフォルト値は`4`。
- `--env-timelimit <int>`:ラウンドごとの時間制限。デフォルト値は`30`。
- `--base-win-reward <int>`:ラウンドに勝った場合の基本報酬。デフォルト値は`999`。
- `--base-lose-reward <int>`:ラウンドに負けた場合の基本報酬。デフォルト値は`-999`。
- `--target-state-size <int>`Targetの状態サイズ。デフォルト値は`6`。
- `--time-state-size <int>`:ゲームの残り時間の状態サイズ。デフォルト値は`1`。
- `--gun-state-size <int>`:銃の状態サイズ。デフォルト値は`1`。
- `--my-state-size <int>`:自分の状態サイズ。デフォルト値は`4`。
- `--total-target-size <int>`全Targetの状態サイズ。デフォルト値は`12`。

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@ -4,41 +4,38 @@ import uuid
from distutils.util import strtobool from distutils.util import strtobool
DEFAULT_SEED = 9331 DEFAULT_SEED = 9331
ENV_PATH = "../Build/3.1.6/Aimbot-ParallelEnv" ENV_PATH = "../Build/3.6/Aimbot-ParallelEnv"
WAND_ENTITY = "koha9" WAND_ENTITY = "koha9"
WORKER_ID = 1 WORKER_ID = 1
BASE_PORT = 1000 BASE_PORT = 1000
# tensorboard names # tensorboard names
GAME_NAME = "Aimbot_Target_Hybrid_PMNN_V3"
GAME_TYPE = "Mix_Verification"
# max round steps per agent is 2500/Decision_period, 25 seconds # max round steps per agent is 2500/Decision_period, 25 seconds
TOTAL_STEPS = 3150000 TOTAL_STEPS = 3150000
BATCH_SIZE = 512 BATCH_SIZE = 512
MAX_TRAINNING_DATASETS = 6000 MAX_TRAINNING_DATASETS = 6000
DECISION_PERIOD = 1 DECISION_PERIOD = 1
LEARNING_RATE = 6.5e-4 LEARNING_RATE = 5e-5
GAMMA = 0.99 GAMMA = 0.999
GAE_LAMBDA = 0.95 GAE_LAMBDA = 0.95
EPOCHS = 3 EPOCHS = 3
CLIP_COEF = 0.11 CLIP_COEF = 0.11
LOSS_COEF = [1.0, 1.0, 1.0, 1.0] # free go attack defence LOSS_COEF = [1.0, 1.0, 1.0, 1.0] # free go attack defence
POLICY_COEF = [1.0, 1.0, 1.0, 1.0] POLICY_COEF = [0.8, 0.8, 0.8, 0.8]
ENTROPY_COEF = [0.05, 0.05, 0.05, 0.05] ENTROPY_COEF = [0.05, 0.05, 0.05, 0.05]
CRITIC_COEF = [0.5, 0.5, 0.5, 0.5] CRITIC_COEF = [1.0, 1.0, 1.0, 1.0]
TARGET_LEARNING_RATE = 1e-6 TARGET_LEARNING_RATE = 1e-6
FREEZE_VIEW_NETWORK = False FREEZE_VIEW_NETWORK = False
BROADCASTREWARD = False
ANNEAL_LEARNING_RATE = True ANNEAL_LEARNING_RATE = True
CLIP_VLOSS = True CLIP_VLOSS = True
NORM_ADV = False NORM_ADV = False
TRAIN = False TRAIN = True
SAVE_MODEL = False SAVE_MODEL = True
WANDB_TACK = False WANDB_TACK = True
LOAD_DIR = None LOAD_DIR = None
#LOAD_DIR = "../PPO-Model/PList_Go_LeakyReLU_9331_1677965178_bestGoto/PList_Go_LeakyReLU_9331_1677965178_10.709002.pt" # LOAD_DIR = "../PPO-Model/GotoOnly-Level1234_9331_1697122986/8.853553.pt"
# Unity Environment Parameters # Unity Environment Parameters
TARGET_STATE_SIZE = 6 TARGET_STATE_SIZE = 6
@ -53,6 +50,16 @@ TARGETNUM= 4
ENV_TIMELIMIT = 30 ENV_TIMELIMIT = 30
RESULT_BROADCAST_RATIO = 1/ENV_TIMELIMIT RESULT_BROADCAST_RATIO = 1/ENV_TIMELIMIT
save_model_this_episode = False
def is_save_model():
global save_model_this_episode
return save_model_this_episode
def set_save_model(save_model:bool):
print("set save model to ",save_model)
global save_model_this_episode
save_model_this_episode = save_model
def parse_args(): def parse_args():
# fmt: off # fmt: off
# pytorch and environment parameters # pytorch and environment parameters
@ -97,8 +104,6 @@ def parse_args():
help="the number of steps to run in each environment per policy rollout") help="the number of steps to run in each environment per policy rollout")
parser.add_argument("--result-broadcast-ratio", type=float, default=RESULT_BROADCAST_RATIO, parser.add_argument("--result-broadcast-ratio", type=float, default=RESULT_BROADCAST_RATIO,
help="broadcast result when win round is reached,r=result-broadcast-ratio*remainTime") help="broadcast result when win round is reached,r=result-broadcast-ratio*remainTime")
parser.add_argument("--broadCastEndReward", type=lambda x: bool(strtobool(x)), default=BROADCASTREWARD, nargs="?", const=True,
help="save model or not")
# target_learning_rate # target_learning_rate
parser.add_argument("--target-lr", type=float, default=TARGET_LEARNING_RATE, parser.add_argument("--target-lr", type=float, default=TARGET_LEARNING_RATE,
help="target value of downscaling the learning rate") help="target value of downscaling the learning rate")

View File

@ -14,6 +14,8 @@ def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
nn.init.constant_(layer.bias, bias_const) nn.init.constant_(layer.bias, bias_const)
return layer return layer
neural_size_1 = 400
neural_size_2 = 300
class PPOAgent(nn.Module): class PPOAgent(nn.Module):
def __init__( def __init__(
@ -31,99 +33,76 @@ class PPOAgent(nn.Module):
self.unity_action_size = env.unity_action_size self.unity_action_size = env.unity_action_size
self.state_size = self.unity_observation_shape[0] self.state_size = self.unity_observation_shape[0]
self.agent_num = env.unity_agent_num self.agent_num = env.unity_agent_num
self.target_size = self.args.target_state_size
self.time_state_size = self.args.time_state_size
self.gun_state_size = self.args.gun_state_size
self.my_state_size = self.args.my_state_size
self.ray_state_size = env.unity_observation_shape[0] - self.args.total_target_size
self.state_size_without_ray = self.args.total_target_size
self.head_input_size = (
env.unity_observation_shape[0] - self.target_size - self.time_state_size - self.gun_state_size
) # except target state input
self.unity_discrete_type = env.unity_discrete_type self.unity_discrete_type = env.unity_discrete_type
self.discrete_size = env.unity_discrete_size self.discrete_size = env.unity_discrete_size
self.discrete_shape = list(env.unity_discrete_branches) self.discrete_shape = list(env.unity_discrete_branches)
self.continuous_size = env.unity_continuous_size self.continuous_size = env.unity_continuous_size
self.view_network = nn.Sequential(layer_init(nn.Linear(self.ray_state_size, 200)), nn.LeakyReLU()) self.hidden_networks = nn.ModuleList(
self.target_networks = nn.ModuleList(
[ [
nn.Sequential(layer_init(nn.Linear(self.state_size_without_ray, 100)), nn.LeakyReLU()) nn.Sequential(
for i in range(self.target_num) layer_init(nn.Linear(self.state_size, neural_size_1)),
] nn.LeakyReLU(),
) layer_init(nn.Linear(neural_size_1, neural_size_2)),
self.middle_networks = nn.ModuleList( nn.LeakyReLU(),
[ )
nn.Sequential(layer_init(nn.Linear(300, 200)), nn.LeakyReLU())
for i in range(self.target_num) for i in range(self.target_num)
] ]
) )
self.actor_dis = nn.ModuleList( self.actor_dis = nn.ModuleList(
[layer_init(nn.Linear(200, self.discrete_size), std=0.5) for i in range(self.target_num)] [layer_init(nn.Linear(neural_size_2, self.discrete_size), std=0.5) for i in range(self.target_num)]
) )
self.actor_mean = nn.ModuleList( self.actor_mean = nn.ModuleList(
[layer_init(nn.Linear(200, self.continuous_size), std=0.5) for i in range(self.target_num)] [layer_init(nn.Linear(neural_size_2, self.continuous_size), std=0) for i in range(self.target_num)]
) )
self.actor_logstd = nn.ParameterList( self.actor_logstd = nn.ParameterList(
[nn.Parameter(torch.zeros(1, self.continuous_size)) for i in range(self.target_num)] [nn.Parameter(torch.zeros(1, self.continuous_size)) for i in range(self.target_num)]
) # nn.Parameter(torch.zeros(1, self.continuous_size)) )
self.critic = nn.ModuleList( self.critic = nn.ModuleList(
[layer_init(nn.Linear(200, 1), std=1) for i in range(self.target_num)] [layer_init(nn.Linear(neural_size_2, 1), std=0) for i in range(self.target_num)]
) )
def get_value(self, state: torch.Tensor): def get_value(self, state: torch.Tensor):
# get critic value
# state.size()[0] is batch_size
target = state[:, 0].to(torch.int32) # int target = state[:, 0].to(torch.int32) # int
this_state_num = target.size()[0] hidden_output = torch.stack(
view_input = state[:, -self.ray_state_size:] # all ray input [self.hidden_networks[target[i]](state[i]) for i in range(state.size()[0])]
target_input = state[:, : self.state_size_without_ray]
view_layer = self.view_network(view_input)
target_layer = torch.stack(
[self.target_networks[target[i]](target_input[i]) for i in range(this_state_num)]
)
middle_input = torch.cat([view_layer, target_layer], dim=1)
middle_layer = torch.stack(
[self.middle_networks[target[i]](middle_input[i]) for i in range(this_state_num)]
) )
criticV = torch.stack( criticV = torch.stack(
[self.critic[target[i]](middle_layer[i]) for i in range(this_state_num)] [self.critic[target[i]](hidden_output[i]) for i in range(state.size()[0])]
) # self.critic )
return criticV return criticV
def get_actions_value(self, state: torch.Tensor, actions=None): def get_actions_value(self, state: torch.Tensor, actions=None):
# get actions and value
target = state[:, 0].to(torch.int32) # int target = state[:, 0].to(torch.int32) # int
this_state_num = target.size()[0] hidden_output = torch.stack(
view_input = state[:, -self.ray_state_size:] # all ray input [self.hidden_networks[target[i]](state[i]) for i in range(target.size()[0])]
target_input = state[:, : self.state_size_without_ray]
view_layer = self.view_network(view_input)
target_layer = torch.stack(
[self.target_networks[target[i]](target_input[i]) for i in range(this_state_num)]
)
middle_input = torch.cat([view_layer, target_layer], dim=1)
middle_layer = torch.stack(
[self.middle_networks[target[i]](middle_input[i]) for i in range(this_state_num)]
) )
# discrete # discrete
# 递归targets的数量,既agent数来实现根据target不同来选用对应的输出网络计算输出 # 递归targets的数量,既agent数来实现根据target不同来选用对应的输出网络计算输出
dis_logits = torch.stack( dis_logits = torch.stack(
[self.actor_dis[target[i]](middle_layer[i]) for i in range(this_state_num)] [self.actor_dis[target[i]](hidden_output[i]) for i in range(target.size()[0])]
) )
split_logits = torch.split(dis_logits, self.discrete_shape, dim=1) split_logits = torch.split(dis_logits, self.discrete_shape, dim=1)
multi_categoricals = [Categorical(logits=thisLogits) for thisLogits in split_logits] multi_categoricals = [Categorical(logits=thisLogits) for thisLogits in split_logits]
# continuous # continuous
actions_mean = torch.stack( actions_mean = torch.stack(
[self.actor_mean[target[i]](middle_layer[i]) for i in range(this_state_num)] [self.actor_mean[target[i]](hidden_output[i]) for i in range(target.size()[0])]
) # self.actor_mean(hidden) ) # self.actor_mean(hidden)
action_logstd = torch.stack( action_logstd = torch.stack(
[torch.squeeze(self.actor_logstd[target[i]], 0) for i in range(this_state_num)] [torch.squeeze(self.actor_logstd[target[i]], 0) for i in range(target.size()[0])]
) )
# print(action_logstd) # print(action_logstd)
action_std = torch.exp(action_logstd) # torch.exp(action_logstd) action_std = torch.exp(action_logstd) # torch.exp(action_logstd)
con_probs = Normal(actions_mean, action_std) con_probs = Normal(actions_mean, action_std)
# critic # critic
criticV = torch.stack( criticV = torch.stack(
[self.critic[target[i]](middle_layer[i]) for i in range(this_state_num)] [self.critic[target[i]](hidden_output[i]) for i in range(target.size()[0])]
) # self.critic ) # self.critic
if actions is None: if actions is None:
@ -275,8 +254,8 @@ class PPOAgent(nn.Module):
self, self,
rewards: torch.Tensor, rewards: torch.Tensor,
dones: torch.Tensor, dones: torch.Tensor,
values: torch.tensor, values: torch.Tensor,
next_obs: torch.tensor, next_obs: torch.Tensor,
next_done: torch.Tensor, next_done: torch.Tensor,
) -> tuple: ) -> tuple:
# GAE # GAE