# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_continuous_actionpy import argparse import copy import os import random import time from distutils.util import strtobool from typing import Callable import gymnasium as gym import numpy as np import torch import torch.nn as nn import torch.optim as optim from torch.distributions.normal import Normal from torch.utils.tensorboard import SummaryWriter def parse_args(): # fmt: off parser = argparse.ArgumentParser() parser.add_argument("--exp-name", type=str, default=os.path.basename(__file__).rstrip(".py"), help="the name of this experiment") parser.add_argument("--seed", type=int, default=1, help="seed of the experiment") parser.add_argument("--torch-deterministic", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True, help="if toggled, `torch.backends.cudnn.deterministic=False`") 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("--track", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True, help="if toggled, this experiment will be tracked with Weights and Biases") parser.add_argument("--wandb-project-name", type=str, default="cleanRL", help="the wandb's project name") parser.add_argument("--wandb-entity", type=str, default=None, help="the entity (team) of wandb's project") parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True, help="whether to capture videos of the agent performances (check out `videos` folder)") parser.add_argument("--save-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True, help="whether to save model into the `runs/{run_name}` folder") parser.add_argument("--upload-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True, help="whether to upload the saved model to huggingface") parser.add_argument("--hf-entity", type=str, default="", help="the user or org name of the model repository from the Hugging Face Hub") # Algorithm specific arguments parser.add_argument("--env-id", type=str, default="HalfCheetah-v4", help="the id of the environment") parser.add_argument("--total-timesteps", type=int, default=1000000, help="total timesteps of the experiments") parser.add_argument("--learning-rate", type=float, default=3e-4, help="the learning rate of the optimizer") parser.add_argument("--num-envs", type=int, default=1, help="the number of parallel game environments") parser.add_argument("--num-steps", type=int, default=2048, help="the number of steps to run in each environment per policy rollout") parser.add_argument("--anneal-lr", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True, help="Toggle learning rate annealing for policy and value networks") parser.add_argument("--gamma", type=float, default=0.99, help="the discount factor gamma") parser.add_argument("--gae-lambda", type=float, default=0.95, help="the lambda for the general advantage estimation") parser.add_argument("--num-minibatches", type=int, default=32, help="the number of mini-batches") parser.add_argument("--update-epochs", type=int, default=10, help="the K epochs to update the policy") parser.add_argument("--norm-adv", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True, help="Toggles advantages normalization") parser.add_argument("--clip-coef", type=float, default=0.2, help="the surrogate clipping coefficient") parser.add_argument("--clip-vloss", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True, help="Toggles whether or not to use a clipped loss for the value function, as per the paper.") parser.add_argument("--ent-coef", type=float, default=0.0, help="coefficient of the entropy") parser.add_argument("--vf-coef", type=float, default=0.5, help="coefficient of the value function") 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") args = parser.parse_args() args.batch_size = int(args.num_envs * args.num_steps) args.minibatch_size = int(args.batch_size // args.num_minibatches) # fmt: on return args # https://github.com/Farama-Foundation/Gymnasium/blob/main/gymnasium/wrappers/normalize.py class RunningMeanStd(nn.Module): def __init__(self, epsilon=1e-4, shape=()): super().__init__() self.register_buffer("mean", torch.zeros(shape, dtype=torch.float64)) self.register_buffer("var", torch.ones(shape, dtype=torch.float64)) self.register_buffer("count", torch.tensor(epsilon, dtype=torch.float64)) def update(self, x): x = torch.as_tensor(x, dtype=torch.float64).to(self.mean.device) batch_mean = torch.mean(x, dim=0).to(self.mean.device) batch_var = torch.var(x, dim=0, unbiased=False).to(self.mean.device) batch_count = x.shape[0] self.mean, self.var, self.count = update_mean_var_count_from_moments( self.mean, self.var, self.count, batch_mean, batch_var, batch_count ) def update_mean_var_count_from_moments(mean, var, count, batch_mean, batch_var, batch_count): delta = batch_mean - mean tot_count = count + batch_count new_mean = mean + delta * batch_count / tot_count m_a = var * count m_b = batch_var * batch_count M2 = m_a + m_b + torch.square(delta) * count * batch_count / tot_count new_var = M2 / tot_count new_count = tot_count return new_mean, new_var, new_count class NormalizeObservation(gym.Wrapper, gym.utils.RecordConstructorArgs): def __init__(self, env: gym.Env, epsilon: float = 1e-8): gym.utils.RecordConstructorArgs.__init__(self, epsilon=epsilon) gym.Wrapper.__init__(self, env) try: self.num_envs = self.get_wrapper_attr("num_envs") self.is_vector_env = self.get_wrapper_attr("is_vector_env") except AttributeError: self.num_envs = 1 self.is_vector_env = False if self.is_vector_env: self.obs_rms = RunningMeanStd(shape=self.single_observation_space.shape) else: self.obs_rms = RunningMeanStd(shape=self.observation_space.shape) self.epsilon = epsilon self.enable = True self.freeze = False def step(self, action): obs, rews, terminateds, truncateds, infos = self.env.step(action) if self.is_vector_env: obs = self.normalize(obs) else: obs = self.normalize(np.array([obs]))[0] return obs, rews, terminateds, truncateds, infos def reset(self, **kwargs): obs, info = self.env.reset(**kwargs) if self.is_vector_env: return self.normalize(obs), info else: return self.normalize(np.array([obs]))[0], info def normalize(self, obs): if not self.freeze: self.obs_rms.update(obs) if self.enable: return (obs - self.obs_rms.mean.cpu().numpy()) / np.sqrt(self.obs_rms.var.cpu().numpy() + self.epsilon) return obs class NormalizeReward(gym.core.Wrapper, gym.utils.RecordConstructorArgs): def __init__( self, env: gym.Env, gamma: float = 0.99, epsilon: float = 1e-8, ): gym.utils.RecordConstructorArgs.__init__(self, gamma=gamma, epsilon=epsilon) gym.Wrapper.__init__(self, env) try: self.num_envs = self.get_wrapper_attr("num_envs") self.is_vector_env = self.get_wrapper_attr("is_vector_env") except AttributeError: self.num_envs = 1 self.is_vector_env = False self.return_rms = RunningMeanStd(shape=()) self.returns = np.zeros(self.num_envs) self.gamma = gamma self.epsilon = epsilon self.enable = True self.freeze = False def step(self, action): obs, rews, terminateds, truncateds, infos = self.env.step(action) if not self.is_vector_env: rews = np.array([rews]) self.returns = self.returns * self.gamma * (1 - terminateds) + rews rews = self.normalize(rews) if not self.is_vector_env: rews = rews[0] return obs, rews, terminateds, truncateds, infos def reset(self, **kwargs): self.returns = np.zeros(self.num_envs) return self.env.reset(**kwargs) def normalize(self, rews): if not self.freeze: self.return_rms.update(self.returns) if self.enable: return rews / np.sqrt(self.return_rms.var.cpu().numpy() + self.epsilon) return rews def get_returns(self): return self.returns def evaluate( model_path: str, make_env: Callable, env_id: str, eval_episodes: int, run_name: str, Model: torch.nn.Module, device: torch.device = torch.device("cpu"), capture_video: bool = True, ): envs = gym.vector.SyncVectorEnv([make_env(env_id, 0, capture_video, run_name)]) agent = Model(envs).to(device) agent.load_state_dict(torch.load(model_path, map_location=device)) agent.eval() envs = gym.vector.SyncVectorEnv([make_env(env_id, 0, capture_video, run_name, agent.obs_rms)]) obs, _ = envs.reset() episodic_returns = [] while len(episodic_returns) < eval_episodes: actions, _, _, _ = agent.get_action_and_value(torch.Tensor(obs).to(device)) next_obs, _, _, _, infos = envs.step(actions.cpu().numpy()) if "final_info" in infos: for info in infos["final_info"]: if "episode" not in info: continue print(f"eval_episode={len(episodic_returns)}, episodic_return={info['episode']['r']}") episodic_returns += [info["episode"]["r"]] obs = next_obs return episodic_returns def make_env(env_id, idx, capture_video, run_name, gamma): def thunk(): if capture_video: env = gym.make(env_id, render_mode="rgb_array") else: env = gym.make(env_id) env = gym.wrappers.FlattenObservation(env) # deal with dm_control's Dict observation space env = gym.wrappers.RecordEpisodeStatistics(env) if capture_video: if idx == 0: env = gym.wrappers.RecordVideo(env, f"videos/{run_name}") env = gym.wrappers.ClipAction(env) env = NormalizeObservation(env) env = gym.wrappers.TransformObservation(env, lambda obs: np.clip(obs, -10, 10)) env = NormalizeReward(env, gamma=gamma) env = gym.wrappers.TransformReward(env, lambda reward: np.clip(reward, -10, 10)) return env return thunk def make_eval_env(env_id, idx, capture_video, run_name, obs_rms=None): def thunk(): if capture_video: env = gym.make(env_id, render_mode="rgb_array") else: env = gym.make(env_id) env = gym.wrappers.FlattenObservation(env) # deal with dm_control's Dict observation space env = gym.wrappers.RecordEpisodeStatistics(env) if capture_video: if idx == 0: env = gym.wrappers.RecordVideo(env, f"videos/{run_name}") env = gym.wrappers.ClipAction(env) env = NormalizeObservation(env) if obs_rms is not None: env.obs_rms = copy.deepcopy(obs_rms) env.freeze = True env = gym.wrappers.TransformObservation(env, lambda obs: np.clip(obs, -10, 10)) return env return thunk def get_rms(env): obs_rms, return_rms = None, None env_point = env while hasattr(env_point, "env"): if isinstance(env_point, NormalizeObservation): obs_rms = copy.deepcopy(env_point.obs_rms) break env_point = env_point.env else: raise RuntimeError("can't find NormalizeObservation") env_point = env while hasattr(env_point, "env"): if isinstance(env_point, NormalizeReward): return_rms = copy.deepcopy(env_point.return_rms) break env_point = env_point.env else: raise RuntimeError("can't find NormalizeReward") return obs_rms, return_rms 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, envs): super().__init__() self.critic = nn.Sequential( layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)), nn.Tanh(), layer_init(nn.Linear(64, 64)), nn.Tanh(), layer_init(nn.Linear(64, 1), std=1.0), ) self.actor_mean = nn.Sequential( layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)), nn.Tanh(), layer_init(nn.Linear(64, 64)), nn.Tanh(), layer_init(nn.Linear(64, np.prod(envs.single_action_space.shape)), std=0.01), ) self.actor_logstd = nn.Parameter(torch.zeros(1, np.prod(envs.single_action_space.shape))) self.obs_rms = RunningMeanStd(shape=envs.single_observation_space.shape) def get_value(self, x): return self.critic(x) def get_action_and_value(self, x, action=None): action_mean = self.actor_mean(x) action_logstd = self.actor_logstd.expand_as(action_mean) action_std = torch.exp(action_logstd) probs = Normal(action_mean, action_std) if action is None: action = probs.sample() return action, probs.log_prob(action).sum(1), probs.entropy().sum(1), self.critic(x) if __name__ == "__main__": args = parse_args() run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}" if args.track: import wandb wandb.init( project=args.wandb_project_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()])), ) # TRY NOT TO MODIFY: seeding random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) torch.backends.cudnn.deterministic = args.torch_deterministic device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu") # env setup envs = gym.vector.SyncVectorEnv( [make_env(args.env_id, i, args.capture_video, run_name, args.gamma) for i in range(args.num_envs)] ) assert isinstance(envs.single_action_space, gym.spaces.Box), "only continuous action space is supported" agent = Agent(envs).to(device) optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5) # ALGO Logic: Storage setup obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device) actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device) logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device) rewards = torch.zeros((args.num_steps, args.num_envs)).to(device) dones = torch.zeros((args.num_steps, args.num_envs)).to(device) values = torch.zeros((args.num_steps, args.num_envs)).to(device) # TRY NOT TO MODIFY: start the game global_step = 0 start_time = time.time() next_obs, _ = envs.reset(seed=args.seed) next_obs = torch.Tensor(next_obs).to(device) next_done = torch.zeros(args.num_envs).to(device) num_updates = args.total_timesteps // args.batch_size for update in range(1, num_updates + 1): # Annealing the rate if instructed to do so. if args.anneal_lr: frac = 1.0 - (update - 1.0) / num_updates lrnow = frac * args.learning_rate optimizer.param_groups[0]["lr"] = lrnow for step in range(0, args.num_steps): global_step += 1 * args.num_envs obs[step] = next_obs dones[step] = next_done # ALGO LOGIC: action logic with torch.no_grad(): action, logprob, _, value = agent.get_action_and_value(next_obs) values[step] = value.flatten() actions[step] = action logprobs[step] = logprob # TRY NOT TO MODIFY: execute the game and log data. next_obs, reward, terminations, truncations, infos = envs.step(action.cpu().numpy()) done = np.logical_or(terminations, truncations) rewards[step] = torch.tensor(reward).to(device).view(-1) next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device) # https://github.com/DLR-RM/stable-baselines3/pull/658 for idx, trunc in enumerate(truncations): if trunc and not terminations[idx]: real_next_obs = infos["final_observation"][idx] with torch.no_grad(): terminal_value = agent.get_value(torch.Tensor(real_next_obs).to(device)).reshape(1, -1)[0][0] rewards[step][idx] += args.gamma * terminal_value # Only print when at least 1 env is done if "final_info" not in infos: continue for info in infos["final_info"]: # Skip the envs that are not done if info is None: continue print(f"global_step={global_step}, episodic_return={info['episode']['r']}") writer.add_scalar("charts/episodic_return", info["episode"]["r"], global_step) writer.add_scalar("charts/episodic_length", info["episode"]["l"], global_step) # bootstrap value if not done with torch.no_grad(): next_value = agent.get_value(next_obs).reshape(1, -1) advantages = torch.zeros_like(rewards).to(device) lastgaelam = 0 for t in reversed(range(args.num_steps)): if t == args.num_steps - 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.gae_lambda * nextnonterminal * lastgaelam returns = advantages + values # flatten the batch b_obs = obs.reshape((-1,) + envs.single_observation_space.shape) b_logprobs = logprobs.reshape(-1) b_actions = actions.reshape((-1,) + envs.single_action_space.shape) b_advantages = advantages.reshape(-1) b_returns = returns.reshape(-1) b_values = values.reshape(-1) # Optimizing the policy and value network b_inds = np.arange(args.batch_size) clipfracs = [] for epoch in range(args.update_epochs): np.random.shuffle(b_inds) for start in range(0, args.batch_size, args.minibatch_size): end = start + args.minibatch_size mb_inds = b_inds[start:end] _, newlogprob, entropy, newvalue = agent.get_action_and_value(b_obs[mb_inds], b_actions[mb_inds]) logratio = newlogprob - b_logprobs[mb_inds] ratio = logratio.exp() 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()] mb_advantages = b_advantages[mb_inds] if args.norm_adv: mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8) # Policy loss pg_loss1 = -mb_advantages * ratio pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1 + args.clip_coef) pg_loss = torch.max(pg_loss1, pg_loss2).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() entropy_loss = entropy.mean() loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef optimizer.zero_grad() loss.backward() 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 y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy() var_y = np.var(y_true) explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y # TRY NOT TO MODIFY: record rewards for plotting purposes writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step) writer.add_scalar("losses/value_loss", v_loss.item(), global_step) writer.add_scalar("losses/policy_loss", pg_loss.item(), global_step) writer.add_scalar("losses/entropy", entropy_loss.item(), global_step) writer.add_scalar("losses/old_approx_kl", old_approx_kl.item(), global_step) writer.add_scalar("losses/approx_kl", approx_kl.item(), global_step) writer.add_scalar("losses/clipfrac", np.mean(clipfracs), global_step) writer.add_scalar("losses/explained_variance", explained_var, global_step) print("SPS:", int(global_step / (time.time() - start_time))) writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step) if args.save_model: agent.obs_rms = copy.deepcopy(get_rms(envs.envs[0])[0]) model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model" torch.save(agent.state_dict(), model_path) print(f"model saved to {model_path}") episodic_returns = evaluate( model_path, make_eval_env, args.env_id, eval_episodes=10, run_name=f"{run_name}-eval", Model=Agent, device=device, ) for idx, episodic_return in enumerate(episodic_returns): writer.add_scalar("eval/episodic_return", episodic_return, idx) if args.upload_model: from cleanrl_utils.huggingface import push_to_hub repo_name = f"{args.env_id}-{args.exp_name}-seed{args.seed}" repo_id = f"{args.hf_entity}/{repo_name}" if args.hf_entity else repo_name push_to_hub(args, episodic_returns, repo_id, "PPO", f"runs/{run_name}", f"videos/{run_name}-eval") envs.close() writer.close()