import argparse import os import time import gymnasium as gym import numpy as np import torch from stable_baselines3.common.atari_wrappers import FireResetEnv, EpisodicLifeEnv, ClipRewardEnv from torch import nn, optim from torch.distributions import Categorical from torch.nn.utils.clip_grad import clip_grad_norm_ from torch.utils.tensorboard.writer import SummaryWriter from tqdm import tqdm def get_args(): parser = argparse.ArgumentParser() parser.add_argument('--exp_name', type=str, default=os.path.basename(__file__).rstrip('.py')) parser.add_argument('--env_id', type=str, default='ALE/Breakout-v5') parser.add_argument('--seed', type=int, default=1) parser.add_argument('--use_cuda', type=bool, default=True) parser.add_argument('--learning_rate', type=float, default=2.5e-4) parser.add_argument('--lr_decay', type=bool, default=True) parser.add_argument('--total_steps', type=int, default=int(1e7)) parser.add_argument('--num_envs', type=int, default=8) parser.add_argument('--num_steps', type=int, default=128) parser.add_argument('--update_epochs', type=int, default=8) parser.add_argument('--num_mini_batches', type=int, default=4) parser.add_argument('--gae_lambda', type=float, default=0.95) parser.add_argument('--gamma', type=float, default=0.99) parser.add_argument('--clip_value_loss', type=bool, default=True) parser.add_argument('--c_1', type=float, default=1.0) parser.add_argument('--c_2', type=float, default=0.01) parser.add_argument('--clip_grad_norm', type=float, default=0.5) parser.add_argument('--beta', type=float, default=1.0) args = parser.parse_args() args.device = torch.device('cuda' if torch.cuda.is_available() and args.use_cuda else 'cpu') args.batch_size = int(args.num_envs * args.num_steps) args.minibatch_size = int(args.batch_size // args.num_mini_batches) args.num_updates = int(args.total_steps // args.batch_size) return args def make_env(env_id): def thunk(): env = gym.make(env_id, frameskip=1, repeat_action_probability=0.0, full_action_space=False) env = gym.wrappers.RecordEpisodeStatistics(env) if 'FIRE' in env.unwrapped.get_action_meanings(): env = FireResetEnv(env) env = EpisodicLifeEnv(env) env = ClipRewardEnv(env) env = gym.wrappers.AtariPreprocessing(env, scale_obs=True) env = gym.wrappers.FrameStack(env, 4) return env return thunk def compute_advantages(rewards, flags, values, last_value, args): advantages = torch.zeros((args.num_steps, args.num_envs)).to(args.device) adv = torch.zeros(args.num_envs).to(args.device) for i in reversed(range(args.num_steps)): returns = rewards[i] + args.gamma * flags[i] * last_value delta = returns - values[i] adv = delta + args.gamma * args.gae_lambda * flags[i] * adv advantages[i] = adv last_value = values[i] return advantages class Buffer: def __init__(self, num_steps, num_envs, observation_shape, action_dim, device): self.states = np.zeros((num_steps, num_envs, *observation_shape), dtype=np.float32) self.actions = np.zeros((num_steps, num_envs), dtype=np.int64) self.rewards = np.zeros((num_steps, num_envs), dtype=np.float32) self.flags = np.zeros((num_steps, num_envs), dtype=np.float32) self.log_probs = np.zeros((num_steps, num_envs), dtype=np.float32) self.probs = np.zeros((num_steps, num_envs, action_dim), dtype=np.float32) self.values = np.zeros((num_steps, num_envs), dtype=np.float32) self.step = 0 self.num_steps = num_steps self.device = device def push(self, state, action, reward, flag, log_prob, prob, value): self.states[self.step] = state self.actions[self.step] = action self.rewards[self.step] = reward self.flags[self.step] = flag self.log_probs[self.step] = log_prob self.probs[self.step] = prob self.values[self.step] = value self.step = (self.step + 1) % self.num_steps def get(self): return ( torch.from_numpy(self.states).to(self.device), torch.from_numpy(self.actions).to(self.device), torch.from_numpy(self.rewards).to(self.device), torch.from_numpy(self.flags).to(self.device), torch.from_numpy(self.log_probs).to(self.device), torch.from_numpy(self.values).to(self.device), ) def get_probs(self): return torch.from_numpy(self.probs).to(self.device) 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 Agent(nn.Module): def __init__(self, action_dim, device): super().__init__() self.encoder = nn.Sequential( layer_init(nn.Conv2d(4, 32, 8, stride=4)), nn.ReLU(), layer_init(nn.Conv2d(32, 64, 4, stride=2)), nn.ReLU(), layer_init(nn.Conv2d(64, 64, 3, stride=1)), nn.ReLU(), nn.Flatten(), layer_init(nn.Linear(64 * 7 * 7, 512)), nn.ReLU() ) self.actor_net = layer_init(nn.Linear(512, action_dim), std=0.01) self.critic_net = layer_init(nn.Linear(512, 1), std=1) if device.type == 'cuda': self.cuda() def forward(self, state): hidden = self.encoder(state) actor_value = self.actor_net(hidden) distribution = Categorical(logits=actor_value) action = distribution.sample() log_prob = distribution.log_prob(action) value = self.critic_net(hidden).squeeze(-1) return action, log_prob, value, distribution.probs def evaluate(self, states, actions): hidden = self.encoder(states) actor_values = self.actor_net(hidden) distribution = Categorical(logits=actor_values) log_probs = distribution.log_prob(actions) entropy = distribution.entropy() values = self.critic_net(hidden).squeeze(-1) return log_probs, values, entropy, distribution.probs def critic(self, state): return self.critic_net(self.encoder(state)).squeeze(-1) def train(env_id, seed): args = get_args() args.env_id = env_id args.seed = seed run_name = ( 'ppo_penalty' + '_epoch_' + str(args.update_epochs) + '_seed_' + str(args.seed) ) # 保存训练日志 path_string = str(args.env_id)[4:] + '/' + run_name writer = SummaryWriter(path_string) writer.add_text( 'Hyperparameter', '|param|value|\n|-|-|\n%s' % ('\n'.join([f'|{key}|{value}|' for key, value in vars(args).items()])), ) # 初始化并行环境 envs = gym.vector.AsyncVectorEnv([make_env(args.env_id) for _ in range(args.num_envs)]) # 状态空间和动作空间 observation_shape = envs.single_observation_space.shape action_dim = envs.single_action_space.n # 随机数种子 if args.seed: numpy_rng = np.random.default_rng(args.seed) torch.manual_seed(args.seed) state, _ = envs.reset(seed=args.seed) else: numpy_rng = np.random.default_rng() state, _ = envs.reset() # 价值网络和策略网络 agent = Agent(action_dim, args.device) # 优化器 optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate) # 存储数据的buffer rollout_buffer = Buffer(args.num_steps, args.num_envs, observation_shape, action_dim, args.device) global_step = 0 start_time = time.time() # 开始收集数据 for _ in tqdm(range(args.num_updates)): # 学习率线性递减 if args.lr_decay: optimizer.param_groups[0]['lr'] -= (args.learning_rate - 1e-12) / args.num_updates for _ in range(args.num_steps): global_step += 1 * args.num_envs with torch.no_grad(): action, log_prob, value, prob = agent(torch.from_numpy(state).to(args.device).float()) action = action.cpu().numpy() next_state, reward, terminated, truncated, all_info = envs.step(action) # 存储数据 flag = 1.0 - np.logical_or(terminated, truncated) log_prob = log_prob.cpu().numpy() prob = prob.cpu().numpy() value = value.cpu().numpy() rollout_buffer.push(state, action, reward, flag, log_prob, prob, value) state = next_state if 'final_info' not in all_info: continue # 写入训练过程的数据 for info in all_info['final_info']: if info is None: continue if 'episode' in info.keys(): writer.add_scalar('charts/episodic_return', info['episode']['r'], global_step) # print(float(info['episode']['r'])) break # ------------------------------- 上面收集了足够的数据，下面开始更新 ------------------------------- # states, actions, rewards, flags, log_probs, values = rollout_buffer.get() probs = rollout_buffer.get_probs() with torch.no_grad(): last_value = agent.critic(torch.from_numpy(next_state).to(args.device).float()) # 计算优势值和TD目标 advantages = compute_advantages(rewards, flags, values, last_value, args) td_target = advantages + values # 将数据展平 states = states.reshape(-1, *observation_shape) actions = actions.reshape(-1) log_probs = log_probs.reshape(-1) probs = probs.reshape((-1, action_dim)) td_target = td_target.reshape(-1) advantages = advantages.reshape(-1) values = values.reshape(-1) batch_indexes = np.arange(args.batch_size) # 更新策略网络和价值网络 for e in range(1, args.update_epochs + 1): numpy_rng.shuffle(batch_indexes) for start in range(0, args.batch_size, args.minibatch_size): end = start + args.minibatch_size index = batch_indexes[start:end] # 得到最新的策略网络和价值网络输出 new_log_probs, td_predict, entropy, new_probs = agent.evaluate(states[index], actions[index]) log_ratio = new_log_probs - log_probs[index] ratios = log_ratio.exp() # 计算kl散度 d = torch.sum( probs[index] * torch.log((probs[index] + 1e-12) / (new_probs + 1e-12)), 1 ) writer.add_scalar('charts/average_kld', d.mean(), global_step) writer.add_scalar('others/min_kld', d.min(), global_step) writer.add_scalar('others/max_kld', d.max(), global_step) # 优势值标准化 b_advantages = advantages[index] b_advantages = (b_advantages - b_advantages.mean()) / (b_advantages.std() + 1e-12) # 策略网络损失 if d.mean() < 0.02 / 1.5: args.beta = args.beta / 2 if d.mean() > 0.02 * 1.5: args.beta = args.beta * 2 policy_loss = -(b_advantages * ratios - args.beta * d).mean() # 价值网络损失 if args.clip_value_loss: v_loss_un_clipped = (td_predict - td_target[index]) ** 2 v_clipped = td_target[index] + torch.clamp( td_predict - td_target[index], -0.2, 0.2, ) v_loss_clipped = (v_clipped - td_target[index]) ** 2 v_loss_max = torch.max(v_loss_un_clipped, v_loss_clipped) value_loss = 0.5 * v_loss_max.mean() else: value_loss = 0.5 * ((td_predict - td_target[index]) ** 2).mean() entropy_loss = entropy.mean() # 保存训练过程中的一些数据 writer.add_scalar('losses/value_loss', value_loss.item(), global_step) writer.add_scalar('losses/policy_loss', policy_loss.item(), global_step) writer.add_scalar('losses/entropy', entropy_loss.item(), global_step) writer.add_scalar('losses/delta', torch.abs(ratios - 1).mean().item(), global_step) # 总的损失 loss = policy_loss + value_loss * args.c_1 - entropy_loss * args.c_2 # 更新网络参数 optimizer.zero_grad() loss.backward() clip_grad_norm_(agent.parameters(), args.clip_grad_norm) optimizer.step() explained_var = ( np.nan if torch.var(td_target) == 0 else 1 - torch.var(td_target - values) / torch.var(td_target) ) writer.add_scalar('charts/learning_rate', optimizer.param_groups[0]['lr'], global_step) writer.add_scalar('charts/SPS', int(global_step / (time.time() - start_time)), global_step) writer.add_scalar('others/explained_var', explained_var, global_step) envs.close() writer.close() def main(): for env_id in ['Breakout']: for seed in [1, 2, 3]: print(env_id, seed) train('ALE/' + env_id + '-v5', seed) if __name__ == '__main__': main()