import argparse
import wandb
import time
import numpy as np
import random
import uuid
import torch
import torch.nn as nn
import torch.optim as optim
import atexit
from torchviz import make_dot, make_dot_from_trace
from AimbotEnv import Aimbot
from tqdm import tqdm
from enum import Enum
from torch.distributions.normal import Normal
from torch.distributions.categorical import Categorical
from distutils.util import strtobool
from torch.utils.tensorboard import SummaryWriter
from mlagents_envs.environment import UnityEnvironment
from mlagents_envs.side_channel.side_channel import (
SideChannel,
IncomingMessage,
OutgoingMessage,
)
from typing import List
bestReward = -1
DEFAULT_SEED = 9331
ENV_PATH = "../Build/Build-ParallelEnv-Target-OffPolicy-SingleStack-SideChannel-EndReward-Easy-V2.7-FreeOnly-NormalMapSize/Aimbot-ParallelEnv"
SIDE_CHANNEL_UUID = uuid.UUID("8bbfb62a-99b4-457c-879d-b78b69066b5e")
WAND_ENTITY = "koha9"
WORKER_ID = 2
BASE_PORT = 1111
# max round steps per agent is 2500/Decision_period, 25 seconds
# !!!check every parameters before run!!!
TOTAL_STEPS = 3150000
BATCH_SIZE = 1024
MAX_TRAINNING_DATASETS = 6000
DECISION_PERIOD = 1
LEARNING_RATE = 5e-4
GAMMA = 0.99
GAE_LAMBDA = 0.95
EPOCHS = 3
CLIP_COEF = 0.11
LOSS_COEF = [1.0, 1.0, 1.0, 1.0] # free go attack defence
POLICY_COEF = [1.0, 1.0, 1.0, 1.0]
ENTROPY_COEF = [0.05, 0.05, 0.05, 0.05]
CRITIC_COEF = [0.5, 0.5, 0.5, 0.5]
TARGET_LEARNING_RATE = 1e-6
FREEZE_VIEW_NETWORK = False
ANNEAL_LEARNING_RATE = True
CLIP_VLOSS = True
NORM_ADV = True
TRAIN = True
SAVE_MODEL = False
WANDB_TACK = False
LOAD_DIR = None
#LOAD_DIR = "../PPO-Model/Aimbot_Target_Hybrid_PMNN_V2_OffPolicy_EndBC_9331_1670986948-freeonly-20/Aimbot_Target_Hybrid_PMNN_V2_OffPolicy_EndBC_9331_1670986948_0.7949778.pt"
# public data
class Targets(Enum):
Free = 0
Go = 1
Attack = 2
Defence = 3
Num = 4
TARGET_STATE_SIZE = 6
INAREA_STATE_SIZE = 1
TIME_STATE_SIZE = 1
GUN_STATE_SIZE = 1
MY_STATE_SIZE = 4
TOTAL_T_SIZE = TARGET_STATE_SIZE+INAREA_STATE_SIZE+TIME_STATE_SIZE+GUN_STATE_SIZE+MY_STATE_SIZE
BASE_WINREWARD = 999
BASE_LOSEREWARD = -999
TARGETNUM= 4
ENV_TIMELIMIT = 30
RESULT_BROADCAST_RATIO = 1/ENV_TIMELIMIT
TotalRounds = {"Free":0,"Go":0,"Attack":0}
WinRounds = {"Free":0,"Go":0,"Attack":0}
# !!!SPECIAL PARAMETERS!!!
# change it while program is finished
using_targets_num = 3
def parse_args():
# fmt: off
# pytorch and environment parameters
parser = argparse.ArgumentParser()
parser.add_argument("--seed", type=int, default=DEFAULT_SEED,
help="seed of the experiment")
parser.add_argument("--path", type=str, default=ENV_PATH,
help="enviroment path")
parser.add_argument("--workerID", type=int, default=WORKER_ID,
help="unity worker ID")
parser.add_argument("--baseport", type=int, default=BASE_PORT,
help="port to connect to Unity environment")
parser.add_argument("--lr", type=float, default=LEARNING_RATE,
help="the learning rate of optimizer")
parser.add_argument("--cuda", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="if toggled, cuda will be enabled by default")
parser.add_argument("--total-timesteps", type=int, default=TOTAL_STEPS,
help="total timesteps of the experiments")
# model parameters
parser.add_argument("--train",type=lambda x: bool(strtobool(x)), default=TRAIN, nargs="?", const=True,
help="Train Model or not")
parser.add_argument("--freeze-viewnet", type=lambda x: bool(strtobool(x)), default=FREEZE_VIEW_NETWORK, nargs="?", const=True,
help="freeze view network or not")
parser.add_argument("--datasetSize", type=int, default=MAX_TRAINNING_DATASETS,
help="training dataset size,start training while dataset collect enough data")
parser.add_argument("--minibatchSize", type=int, default=BATCH_SIZE,
help="nimi batch size")
parser.add_argument("--epochs", type=int, default=EPOCHS,
help="the K epochs to update the policy")
parser.add_argument("--annealLR", type=lambda x: bool(strtobool(x)), default=ANNEAL_LEARNING_RATE, nargs="?", const=True,
help="Toggle learning rate annealing for policy and value networks")
parser.add_argument("--wandb-track", type=lambda x: bool(strtobool(x)), default=WANDB_TACK, nargs="?", const=True,
help="track on the wandb")
parser.add_argument("--save-model", type=lambda x: bool(strtobool(x)), default=SAVE_MODEL, nargs="?", const=True,
help="save model or not")
parser.add_argument("--wandb-entity", type=str, default=WAND_ENTITY,
help="the entity (team) of wandb's project")
parser.add_argument("--load-dir", type=str, default=LOAD_DIR,
help="load model directory")
parser.add_argument("--decision-period", type=int, default=DECISION_PERIOD,
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,
help="broadcast result when win round is reached,r=result-broadcast-ratio*remainTime")
# GAE loss
parser.add_argument("--gae", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="Use GAE for advantage computation")
parser.add_argument("--norm-adv", type=lambda x: bool(strtobool(x)), default=NORM_ADV, nargs="?", const=True,
help="Toggles advantages normalization")
parser.add_argument("--gamma", type=float, default=GAMMA,
help="the discount factor gamma")
parser.add_argument("--gaeLambda", type=float, default=GAE_LAMBDA,
help="the lambda for the general advantage estimation")
parser.add_argument("--clip-coef", type=float, default=CLIP_COEF,
help="the surrogate clipping coefficient")
parser.add_argument("--policy-coef", type=float, default=POLICY_COEF,
help="coefficient of the policy")
parser.add_argument("--ent-coef", type=float, default=ENTROPY_COEF,
help="coefficient of the entropy")
parser.add_argument("--critic-coef", type=float, default=CRITIC_COEF,
help="coefficient of the value function")
parser.add_argument("--clip-vloss", type=lambda x: bool(strtobool(x)), default=CLIP_VLOSS, nargs="?", const=True,
help="Toggles whether or not to use a clipped loss for the value function, as per the paper.")
parser.add_argument("--max-grad-norm", type=float, default=0.5,
help="the maximum norm for the gradient clipping")
parser.add_argument("--target-kl", type=float, default=None,
help="the target KL divergence threshold")
# fmt: on
args = parser.parse_args()
return args
def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
torch.nn.init.orthogonal_(layer.weight, std)
torch.nn.init.constant_(layer.bias, bias_const)
return layer
class PPOAgent(nn.Module):
def __init__(self, env: Aimbot,targetNum:int):
super(PPOAgent, self).__init__()
self.targetNum = targetNum
self.stateSize = env.unity_observation_shape[0]
self.agentNum = env.unity_agent_num
self.targetSize = TARGET_STATE_SIZE
self.timeSize = TIME_STATE_SIZE
self.gunSize = GUN_STATE_SIZE
self.myStateSize = MY_STATE_SIZE
self.raySize = env.unity_observation_shape[0] - TOTAL_T_SIZE
self.nonRaySize = TOTAL_T_SIZE
self.head_input_size = env.unity_observation_shape[0] - self.targetSize-self.timeSize-self.gunSize# except target state input
self.discrete_size = env.unity_discrete_size
self.discrete_shape = list(env.unity_discrete_branches)
self.continuous_size = env.unity_continuous_size
self.viewNetwork = nn.Sequential(
layer_init(nn.Linear(self.raySize, 200)),
nn.Tanh()
)
self.targetNetworks = nn.ModuleList([nn.Sequential(
layer_init(nn.Linear(self.nonRaySize, 100)),
nn.Tanh()
)for i in range(targetNum)])
self.middleNetworks = nn.ModuleList([nn.Sequential(
layer_init(nn.Linear(300,200)),
nn.Tanh()
)for i in range(targetNum)])
self.actor_dis = nn.ModuleList([layer_init(nn.Linear(200, self.discrete_size), std=0.5) for i in range(targetNum)])
self.actor_mean = nn.ModuleList([layer_init(nn.Linear(200, self.continuous_size), std=0.5) for i in range(targetNum)])
# self.actor_logstd = nn.ModuleList([layer_init(nn.Linear(200, self.continuous_size), std=1) for i in range(targetNum)])
# self.actor_logstd = nn.Parameter(torch.zeros(1, self.continuous_size))
self.actor_logstd = nn.ParameterList([nn.Parameter(torch.zeros(1,self.continuous_size))for i in range(targetNum)]) # nn.Parameter(torch.zeros(1, self.continuous_size))
self.critic = nn.ModuleList([layer_init(nn.Linear(200, 1), std=1)for i in range(targetNum)])
def get_value(self, state: torch.Tensor):
target = state[:,0].to(torch.int32) # int
thisStateNum = target.size()[0]
viewInput = state[:,-self.raySize:] # all ray input
targetInput = state[:,:self.nonRaySize]
viewLayer = self.viewNetwork(viewInput)
targetLayer = torch.stack([self.targetNetworks[target[i]](targetInput[i]) for i in range(thisStateNum)])
middleInput = torch.cat([viewLayer,targetLayer],dim = 1)
middleLayer = torch.stack([self.middleNetworks[target[i]](middleInput[i]) for i in range(thisStateNum)])
criticV = torch.stack([self.critic[target[i]](middleLayer[i]) for i in range(thisStateNum)]) # self.critic
return criticV
def get_actions_value(self, state: torch.Tensor, actions=None):
target = state[:,0].to(torch.int32) # int
thisStateNum = target.size()[0]
viewInput = state[:,-self.raySize:] # all ray input
targetInput = state[:,:self.nonRaySize]
viewLayer = self.viewNetwork(viewInput)
targetLayer = torch.stack([self.targetNetworks[target[i]](targetInput[i]) for i in range(thisStateNum)])
middleInput = torch.cat([viewLayer,targetLayer],dim = 1)
middleLayer = torch.stack([self.middleNetworks[target[i]](middleInput[i]) for i in range(thisStateNum)])
# 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
dis_logits = torch.stack([self.actor_dis[target[i]](middleLayer[i]) for i in range(thisStateNum)])
split_logits = torch.split(dis_logits, self.discrete_shape, dim=1)
multi_categoricals = [Categorical(logits=thisLogits) for thisLogits in split_logits]
# continuous
actions_mean = torch.stack([self.actor_mean[target[i]](middleLayer[i]) for i in range(thisStateNum)]) # self.actor_mean(hidden)
# action_logstd = torch.stack([self.actor_logstd[target[i]](middleLayer[i]) for i in range(thisStateNum)]) # self.actor_logstd(hidden)
# action_logstd = self.actor_logstd.expand_as(actions_mean) # self.actor_logstd.expand_as(actions_mean)
action_logstd = torch.stack([torch.squeeze(self.actor_logstd[target[i]],0) for i in range(thisStateNum)])
# print(action_logstd)
action_std = torch.exp(action_logstd) # torch.exp(action_logstd)
con_probs = Normal(actions_mean, action_std)
# critic
criticV = torch.stack([self.critic[target[i]](middleLayer[i]) for i in range(thisStateNum)]) # self.critic
if actions is None:
if args.train:
# select actions base on probability distribution model
disAct = torch.stack([ctgr.sample() for ctgr in multi_categoricals])
conAct = con_probs.sample()
actions = torch.cat([disAct.T, conAct], dim=1)
else:
# select actions base on best probability distribution
disAct = torch.stack([torch.argmax(logit, dim=1) for logit in split_logits])
conAct = actions_mean
actions = torch.cat([disAct.T, conAct], dim=1)
else:
disAct = actions[:, 0 : env.unity_discrete_type].T
conAct = actions[:, env.unity_discrete_type :]
dis_log_prob = torch.stack(
[ctgr.log_prob(act) for act, ctgr in zip(disAct, multi_categoricals)]
)
dis_entropy = torch.stack([ctgr.entropy() for ctgr in multi_categoricals])
return (
actions,
dis_log_prob.sum(0),
dis_entropy.sum(0),
con_probs.log_prob(conAct).sum(1),
con_probs.entropy().sum(1),
criticV,
)
def GAE(agent, args, rewards, dones, values, next_obs, next_done):
# GAE
with torch.no_grad():
next_value = agent.get_value(next_obs).reshape(1, -1)
data_size = rewards.size()[0]
if args.gae:
advantages = torch.zeros_like(rewards).to(device)
lastgaelam = 0
for t in reversed(range(data_size)):
if t == data_size - 1:
nextnonterminal = 1.0 - next_done
nextvalues = next_value
else:
nextnonterminal = 1.0 - dones[t + 1]
nextvalues = values[t + 1]
delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
advantages[t] = lastgaelam = (
delta + args.gamma * args.gaeLambda * nextnonterminal * lastgaelam
)
returns = advantages + values
else:
returns = torch.zeros_like(rewards).to(device)
for t in reversed(range(data_size)):
if t == data_size - 1:
nextnonterminal = 1.0 - next_done
next_return = next_value
else:
nextnonterminal = 1.0 - dones[t + 1]
next_return = returns[t + 1]
returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
advantages = returns - values
return advantages, returns
class AimbotSideChannel(SideChannel):
def __init__(self, channel_id: uuid.UUID) -> None:
super().__init__(channel_id)
def on_message_received(self, msg: IncomingMessage) -> None:
"""
Note: We must implement this method of the SideChannel interface to
receive messages from Unity
"""
thisMessage = msg.read_string()
# print(thisMessage)
thisResult = thisMessage.split("|")
if(thisResult[0] == "result"):
TotalRounds[thisResult[1]]+=1
if(thisResult[2] == "Win"):
WinRounds[thisResult[1]]+=1
#print(TotalRounds)
#print(WinRounds)
elif(thisResult[0] == "Error"):
print(thisMessage)
# 发送函数
def send_string(self, data: str) -> None:
# send a string toC#
msg = OutgoingMessage()
msg.write_string(data)
super().queue_message_to_send(msg)
def send_bool(self, data: bool) -> None:
msg = OutgoingMessage()
msg.write_bool(data)
super().queue_message_to_send(msg)
def send_int(self, data: int) -> None:
msg = OutgoingMessage()
msg.write_int32(data)
super().queue_message_to_send(msg)
def send_float(self, data: float) -> None:
msg = OutgoingMessage()
msg.write_float32(data)
super().queue_message_to_send(msg)
def send_float_list(self, data: List[float]) -> None:
msg = OutgoingMessage()
msg.write_float32_list(data)
super().queue_message_to_send(msg)
def broadCastEndReward(rewardBF:list,remainTime:float):
thisRewardBF = rewardBF
if (rewardBF[-1]<=-500):
# print("Lose DO NOT BROAD CAST",rewardBF[-1])
thisRewardBF[-1] = rewardBF[-1]-BASE_LOSEREWARD
thisRewardBF = thisRewardBF
elif (rewardBF[-1]>=500):
# print("Win! Broadcast reward!",rewardBF[-1])
thisRewardBF[-1] = rewardBF[-1]-BASE_WINREWARD
thisRewardBF = (np.asarray(thisRewardBF)+(remainTime*args.result_broadcast_ratio)).tolist()
else:
print("!!!!!DIDNT GET RESULT REWARD!!!!!!",rewardBF[-1])
return torch.Tensor(thisRewardBF).to(device)
if __name__ == "__main__":
args = parse_args()
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
# Initialize environment anget optimizer
aimBotsideChannel = AimbotSideChannel(SIDE_CHANNEL_UUID);
env = Aimbot(envPath=args.path, workerID=args.workerID, basePort=args.baseport,side_channels=[aimBotsideChannel])
if args.load_dir is None:
agent = PPOAgent(env,TARGETNUM).to(device)
else:
agent = torch.load(args.load_dir)
# freeze
if args.freeze_viewnet:
# freeze the view network
for p in agent.viewNetwork.parameters():
p.requires_grad = False
print("VIEW NETWORK FREEZED")
print("Load Agent", args.load_dir)
print(agent.eval())
optimizer = optim.Adam(agent.parameters(), lr=args.lr, eps=1e-5)
# Tensorboard and WandB Recorder
game_name = "Aimbot_Target_Hybrid_PMNN_V2"
game_type = "OffPolicy_EndBC"
run_name = f"{game_name}_{game_type}_{args.seed}_{int(time.time())}"
if args.wandb_track:
wandb.init(
project=game_name,
entity=args.wandb_entity,
sync_tensorboard=True,
config=vars(args),
name=run_name,
monitor_gym=True,
save_code=True,
)
writer = SummaryWriter(f"runs/{run_name}")
writer.add_text(
"hyperparameters",
"|param|value|\n|-|-|\n%s"
% ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
)
@atexit.register
def save_model():
# save model while exit
saveDir = "../PPO-Model/"+ run_name + "_last.pt"
torch.save(agent, saveDir)
print("save model to " + saveDir)
# Trajectory Buffer
ob_bf = [[] for i in range(env.unity_agent_num)]
act_bf = [[] for i in range(env.unity_agent_num)]
dis_logprobs_bf = [[] for i in range(env.unity_agent_num)]
con_logprobs_bf = [[] for i in range(env.unity_agent_num)]
rewards_bf = [[] for i in range(env.unity_agent_num)]
dones_bf = [[] for i in range(env.unity_agent_num)]
values_bf = [[] for i in range(env.unity_agent_num)]
# start the game
total_update_step = using_targets_num * args.total_timesteps // args.datasetSize
target_steps = [0 for i in range(TARGETNUM)]
start_time = time.time()
state, _, done = env.reset()
# state = torch.Tensor(next_obs).to(device)
# next_done = torch.zeros(env.unity_agent_num).to(device)
# initialize empty training datasets
obs = [torch.tensor([]).to(device) for i in range(TARGETNUM)] # (TARGETNUM,n,env.unity_observation_size)
actions = [torch.tensor([]).to(device) for i in range(TARGETNUM)] # (TARGETNUM,n,env.unity_action_size)
dis_logprobs = [torch.tensor([]).to(device) for i in range(TARGETNUM)] # (TARGETNUM,n,1)
con_logprobs = [torch.tensor([]).to(device) for i in range(TARGETNUM)] # (TARGETNUM,n,1)
rewards = [torch.tensor([]).to(device) for i in range(TARGETNUM)] # (TARGETNUM,n,1)
values = [torch.tensor([]).to(device) for i in range(TARGETNUM)] # (TARGETNUM,n,1)
advantages = [torch.tensor([]).to(device) for i in range(TARGETNUM)] # (TARGETNUM,n,1)
returns = [torch.tensor([]).to(device) for i in range(TARGETNUM)] # (TARGETNUM,n,1)
vis_graph = make_dot(agent.get_actions_value(
torch.Tensor(state).to(device)
), params=dict(agent.named_parameters()))
vis_graph.view() # 会在当前目录下保存一个“Digraph.gv.pdf”文件，并在默认浏览器中打开
with torch.onnx.set_training(agent, False):
trace, _ = torch.jit.get_trace_graph(agent, args=(torch.Tensor(state).to(device),))
make_dot_from_trace(trace)
raise
for total_steps in range(total_update_step):
# discunt learning rate, while step == total_update_step lr will be 0
if args.annealLR:
finalRatio = TARGET_LEARNING_RATE/args.lr
frac = 1.0 - ((total_steps + 1.0) / total_update_step)
lrnow = frac * args.lr
optimizer.param_groups[0]["lr"] = lrnow
else:
lrnow = args.lr
print("new episode",total_steps,"learning rate = ",lrnow)
# MAIN LOOP: run agent in environment
step = 0
training = False
trainQueue = []
last_reward = [0.for i in range(env.unity_agent_num)]
while True:
if step % args.decision_period == 0:
step += 1
# Choose action by agent
with torch.no_grad():
# predict actions
action, dis_logprob, _, con_logprob, _, value = agent.get_actions_value(
torch.Tensor(state).to(device)
)
value = value.flatten()
# variable from GPU to CPU
action_cpu = action.cpu().numpy()
dis_logprob_cpu = dis_logprob.cpu().numpy()
con_logprob_cpu = con_logprob.cpu().numpy()
value_cpu = value.cpu().numpy()
# Environment step
next_state, reward, next_done = env.step(action_cpu)
# save memories
for i in range(env.unity_agent_num):
# save memories to buffers
ob_bf[i].append(state[i])
act_bf[i].append(action_cpu[i])
dis_logprobs_bf[i].append(dis_logprob_cpu[i])
con_logprobs_bf[i].append(con_logprob_cpu[i])
rewards_bf[i].append(reward[i]+last_reward[i])
dones_bf[i].append(done[i])
values_bf[i].append(value_cpu[i])
remainTime = state[i,TARGET_STATE_SIZE]
if next_done[i] == True:
# finished a round, send finished memories to training datasets
# compute advantage and discounted reward
#print(i,"over")
roundTargetType = int(state[i,0])
thisRewardsTensor = broadCastEndReward(rewards_bf[i],remainTime)
adv, rt = GAE(
agent,
args,
thisRewardsTensor,
torch.Tensor(dones_bf[i]).to(device),
torch.tensor(values_bf[i]).to(device),
torch.tensor(next_state[i]).to(device).unsqueeze(0),
torch.Tensor([next_done[i]]).to(device),
)
# send memories to training datasets
obs[roundTargetType] = torch.cat((obs[roundTargetType], torch.tensor(ob_bf[i]).to(device)), 0)
actions[roundTargetType] = torch.cat((actions[roundTargetType], torch.tensor(act_bf[i]).to(device)), 0)
dis_logprobs[roundTargetType] = torch.cat(
(dis_logprobs[roundTargetType], torch.tensor(dis_logprobs_bf[i]).to(device)), 0
)
con_logprobs[roundTargetType] = torch.cat(
(con_logprobs[roundTargetType], torch.tensor(con_logprobs_bf[i]).to(device)), 0
)
rewards[roundTargetType] = torch.cat((rewards[roundTargetType], thisRewardsTensor), 0)
values[roundTargetType] = torch.cat((values[roundTargetType], torch.tensor(values_bf[i]).to(device)), 0)
advantages[roundTargetType] = torch.cat((advantages[roundTargetType], adv), 0)
returns[roundTargetType] = torch.cat((returns[roundTargetType], rt), 0)
# clear buffers
ob_bf[i] = []
act_bf[i] = []
dis_logprobs_bf[i] = []
con_logprobs_bf[i] = []
rewards_bf[i] = []
dones_bf[i] = []
values_bf[i] = []
print(f"train dataset {Targets(roundTargetType).name} added:{obs[roundTargetType].size()[0]}/{args.datasetSize}")
for i in range(TARGETNUM):
if obs[i].size()[0] >= args.datasetSize:
# start train NN
trainQueue.append(i)
if(len(trainQueue)>0):
break
state, done = next_state, next_done
else:
step += 1
# skip this step use last predict action
next_state, reward, next_done = env.step(action_cpu)
# save memories
for i in range(env.unity_agent_num):
if next_done[i] == True:
#print(i,"over???")
# save memories to buffers
ob_bf[i].append(state[i])
act_bf[i].append(action_cpu[i])
dis_logprobs_bf[i].append(dis_logprob_cpu[i])
con_logprobs_bf[i].append(con_logprob_cpu[i])
rewards_bf[i].append(reward[i])
dones_bf[i].append(done[i])
values_bf[i].append(value_cpu[i])
remainTime = state[i,TARGET_STATE_SIZE]
# finished a round, send finished memories to training datasets
# compute advantage and discounted reward
roundTargetType = int(state[i,0])
thisRewardsTensor = broadCastEndReward(rewards_bf[i],remainTime)
adv, rt = GAE(
agent,
args,
thisRewardsTensor,
torch.Tensor(dones_bf[i]).to(device),
torch.tensor(values_bf[i]).to(device),
torch.Tensor(next_state[i]).to(device).unsqueeze(dim = 0),
torch.Tensor([next_done[i]]).to(device),
)
# send memories to training datasets
obs[roundTargetType] = torch.cat((obs[roundTargetType], torch.tensor(ob_bf[i]).to(device)), 0)
actions[roundTargetType] = torch.cat((actions[roundTargetType], torch.tensor(act_bf[i]).to(device)), 0)
dis_logprobs[roundTargetType] = torch.cat(
(dis_logprobs[roundTargetType], torch.tensor(dis_logprobs_bf[i]).to(device)), 0
)
con_logprobs[roundTargetType] = torch.cat(
(con_logprobs[roundTargetType], torch.tensor(con_logprobs_bf[i]).to(device)), 0
)
rewards[roundTargetType] = torch.cat((rewards[roundTargetType], thisRewardsTensor), 0)
values[roundTargetType] = torch.cat((values[roundTargetType], torch.tensor(values_bf[i]).to(device)), 0)
advantages[roundTargetType] = torch.cat((advantages[roundTargetType], adv), 0)
returns[roundTargetType] = torch.cat((returns[roundTargetType], rt), 0)
# clear buffers
ob_bf[i] = []
act_bf[i] = []
dis_logprobs_bf[i] = []
con_logprobs_bf[i] = []
rewards_bf[i] = []
dones_bf[i] = []
values_bf[i] = []
print(f"train dataset {Targets(roundTargetType).name} added:{obs[roundTargetType].size()[0]}/{args.datasetSize}")
state = next_state
last_reward = reward
i += 1
if args.train:
meanRewardList = [] # for WANDB
# loop all tarining queue
for thisT in trainQueue:
target_steps[thisT]+=1
# flatten the batch
b_obs = obs[thisT].reshape((-1,) + env.unity_observation_shape)
b_dis_logprobs = dis_logprobs[thisT].reshape(-1)
b_con_logprobs = con_logprobs[thisT].reshape(-1)
b_actions = actions[thisT].reshape((-1,) + (env.unity_action_size,))
b_advantages = advantages[thisT].reshape(-1)
b_returns = returns[thisT].reshape(-1)
b_values = values[thisT].reshape(-1)
b_size = b_obs.size()[0]
# Optimizing the policy and value network
b_inds = np.arange(b_size)
# clipfracs = []
for epoch in range(args.epochs):
print(epoch,end="")
# shuffle all datasets
np.random.shuffle(b_inds)
for start in range(0, b_size, args.minibatchSize):
print(".",end="")
end = start + args.minibatchSize
mb_inds = b_inds[start:end]
if(np.size(mb_inds)<=1):
break
mb_advantages = b_advantages[mb_inds]
# normalize advantages
if args.norm_adv:
mb_advantages = (mb_advantages - mb_advantages.mean()) / (
mb_advantages.std() + 1e-8
)
(
_,
new_dis_logprob,
dis_entropy,
new_con_logprob,
con_entropy,
newvalue,
) = agent.get_actions_value(b_obs[mb_inds], b_actions[mb_inds])
# discrete ratio
dis_logratio = new_dis_logprob - b_dis_logprobs[mb_inds]
dis_ratio = dis_logratio.exp()
# continuous ratio
con_logratio = new_con_logprob - b_con_logprobs[mb_inds]
con_ratio = con_logratio.exp()
"""
# early stop
with torch.no_grad():
# calculate approx_kl http://joschu.net/blog/kl-approx.html
old_approx_kl = (-logratio).mean()
approx_kl = ((ratio - 1) - logratio).mean()
clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean().item()]
"""
# discrete Policy loss
dis_pg_loss_orig = -mb_advantages * dis_ratio
dis_pg_loss_clip = -mb_advantages * torch.clamp(
dis_ratio, 1 - args.clip_coef, 1 + args.clip_coef
)
dis_pg_loss = torch.max(dis_pg_loss_orig, dis_pg_loss_clip).mean()
# continuous Policy loss
con_pg_loss_orig = -mb_advantages * con_ratio
con_pg_loss_clip = -mb_advantages * torch.clamp(
con_ratio, 1 - args.clip_coef, 1 + args.clip_coef
)
con_pg_loss = torch.max(con_pg_loss_orig, con_pg_loss_clip).mean()
# Value loss
newvalue = newvalue.view(-1)
if args.clip_vloss:
v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
v_clipped = b_values[mb_inds] + torch.clamp(
newvalue - b_values[mb_inds],
-args.clip_coef,
args.clip_coef,
)
v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
v_loss = 0.5 * v_loss_max.mean()
else:
v_loss = 0.5 * ((newvalue - b_returns[mb_inds]) ** 2).mean()
# total loss
entropy_loss = dis_entropy.mean() + con_entropy.mean()
loss = (
dis_pg_loss * POLICY_COEF[thisT]
+ con_pg_loss * POLICY_COEF[thisT]
+ entropy_loss * ENTROPY_COEF[thisT]
+ v_loss * CRITIC_COEF[thisT]
)*LOSS_COEF[thisT]
if(torch.isnan(loss).any()):
print("LOSS Include NAN!!!")
if(torch.isnan(dis_pg_loss.any())):
print("dis_pg_loss include nan")
if(torch.isnan(con_pg_loss.any())):
print("con_pg_loss include nan")
if(torch.isnan(entropy_loss.any())):
print("entropy_loss include nan")
if(torch.isnan(v_loss.any())):
print("v_loss include nan")
raise
optimizer.zero_grad()
loss.backward()
# Clips gradient norm of an iterable of parameters.
nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
optimizer.step()
"""
if args.target_kl is not None:
if approx_kl > args.target_kl:
break
"""
# record mean reward before clear history
print("done")
targetRewardMean = np.mean(rewards[thisT].to("cpu").detach().numpy().copy())
meanRewardList.append(targetRewardMean)
targetName = Targets(thisT).name
# clear this target trainning set buffer
obs[thisT] = torch.tensor([]).to(device)
actions[thisT] = torch.tensor([]).to(device)
dis_logprobs[thisT] = torch.tensor([]).to(device)
con_logprobs[thisT] = torch.tensor([]).to(device)
rewards[thisT] = torch.tensor([]).to(device)
values[thisT] = torch.tensor([]).to(device)
advantages[thisT] = torch.tensor([]).to(device)
returns[thisT] = torch.tensor([]).to(device)
# record rewards for plotting purposes
writer.add_scalar(f"Target{targetName}/value_loss", v_loss.item(), target_steps[thisT])
writer.add_scalar(f"Target{targetName}/dis_policy_loss", dis_pg_loss.item(), target_steps[thisT])
writer.add_scalar(f"Target{targetName}/con_policy_loss", con_pg_loss.item(), target_steps[thisT])
writer.add_scalar(f"Target{targetName}/total_loss", loss.item(), target_steps[thisT])
writer.add_scalar(f"Target{targetName}/entropy_loss", entropy_loss.item(), target_steps[thisT])
writer.add_scalar(f"Target{targetName}/Reward", targetRewardMean, target_steps[thisT])
writer.add_scalar(f"Target{targetName}/WinRatio", WinRounds[targetName]/TotalRounds[targetName], target_steps[thisT])
print(f"episode over Target{targetName} mean reward:", targetRewardMean)
TotalRewardMean = np.mean(meanRewardList)
writer.add_scalar("GlobalCharts/TotalRewardMean", TotalRewardMean, total_steps)
writer.add_scalar("GlobalCharts/learning_rate", optimizer.param_groups[0]["lr"], total_steps)
# New Record!
if TotalRewardMean > bestReward and args.save_model:
bestReward = targetRewardMean
saveDir = "../PPO-Model/" + run_name +"_"+ str(TotalRewardMean) + ".pt"
torch.save(agent, saveDir)
saveDir = "../PPO-Model/"+ run_name + "_last.pt"
torch.save(agent, saveDir)
env.close()
writer.close()