import argparse 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 aimbotEnv import Aimbot from ppoagent import PPOAgent from ppoagent import GAE from ppoagent import AimbotSideChannel from airecorder import WandbRecorder from enum import Enum from distutils.util import strtobool bestReward = -1 SCrecieved = 0 DEFAULT_SEED = 9331 ENV_PATH = "../Build/2.9/Goto-NonNormalization/Aimbot-ParallelEnv" SIDE_CHANNEL_UUID = uuid.UUID("8bbfb62a-99b4-457c-879d-b78b69066b5e") WAND_ENTITY = "koha9" WORKER_ID = 1 BASE_PORT = 1000 # tensorboard names game_name = "Aimbot_Target_Hybrid_PMNN_V3" game_type = "Mix_Verification" # max round steps per agent is 2500/Decision_period, 25 seconds # !!!check every parameters before run!!! TOTAL_STEPS = 3150000 BATCH_SIZE = 512 MAX_TRAINNING_DATASETS = 6000 DECISION_PERIOD = 1 LEARNING_RATE = 6.5e-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 = True BROADCASTREWARD = False ANNEAL_LEARNING_RATE = True CLIP_VLOSS = True NORM_ADV = False TRAIN = True SAVE_MODEL = False WANDB_TACK = False LOAD_DIR = None #LOAD_DIR = "../PPO-Model/PList_Go_LeakyReLU_9331_1677965178_bestGoto/PList_Go_LeakyReLU_9331_1677965178_10.709002.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 # !!!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") parser.add_argument("--broadCastEndReward", type=lambda x: bool(strtobool(x)), default=BROADCASTREWARD, nargs="?", const=True, help="save model or not") # 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 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 elif (rewardBF[-1]>=500): # print("Win! Broadcast reward!",rewardBF[-1]) print(sum(thisRewardBF)/len(thisRewardBF)) 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 = env, trainAgent=args.train, targetNum=TARGETNUM, target_state_size= TARGET_STATE_SIZE, time_state_size=TIME_STATE_SIZE, gun_state_size=GUN_STATE_SIZE, my_state_size=MY_STATE_SIZE, total_t_size=TOTAL_T_SIZE, ).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 run_name = f"{game_type}_{args.seed}_{int(time.time())}" 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 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) 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), 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), 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: # train mode on meanRewardList = [] # for WANDB # loop all tarining queue for thisT in trainQueue: # sart time startTime = time.time() 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 wdb_recorder.add_target_scalar( targetName, thisT, v_loss, dis_pg_loss, con_pg_loss, loss, entropy_loss, targetRewardMean, target_steps, ) print(f"episode over Target{targetName} mean reward:", targetRewardMean) TotalRewardMean = np.mean(meanRewardList) wdb_recorder.add_global_scalar( TotalRewardMean, optimizer.param_groups[0]["lr"], total_steps, ) # print cost time as seconds print("cost time:", time.time() - start_time) # New Record! if TotalRewardMean > bestReward and args.save_model: bestReward = targetRewardMean saveDir = "../PPO-Model/" + run_name +"_"+ str(TotalRewardMean) + ".pt" torch.save(agent, saveDir) else: # train mode off meanRewardList = [] # for WANDB # while not in training mode, clear the buffer for thisT in trainQueue: target_steps[thisT]+=1 targetName = Targets(thisT).name targetRewardMean = np.mean(rewards[thisT].to("cpu").detach().numpy().copy()) meanRewardList.append(targetRewardMean) print(target_steps[thisT]) 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 wdb_recorder.writer.add_scalar(f"Target{targetName}/Reward", targetRewardMean, target_steps[thisT]) wdb_recorder.add_win_ratio(targetName,target_steps[thisT]) print(f"episode over Target{targetName} mean reward:", targetRewardMean) TotalRewardMean = np.mean(meanRewardList) wdb_recorder.writer.add_scalar("GlobalCharts/TotalRewardMean", TotalRewardMean, total_steps) saveDir = "../PPO-Model/"+ run_name + "_last.pt" torch.save(agent, saveDir) env.close() wdb_recorder.writer.close()