import time from typing import List, Tuple, Type, Union, Callable, Optional, Dict, Any import gym from gym import spaces import torch as th import torch.nn.functional as F # Check if tensorboard is available for pytorch # TODO: finish tensorboard integration # try: # from torch.utils.tensorboard import SummaryWriter # except ImportError: # SummaryWriter = None import numpy as np from stable_baselines3.common import logger from stable_baselines3.common.base_class import BaseRLModel from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback from stable_baselines3.common.buffers import RolloutBuffer from stable_baselines3.common.utils import explained_variance, get_schedule_fn from stable_baselines3.common.vec_env import VecEnv from stable_baselines3.common.callbacks import BaseCallback from stable_baselines3.ppo.policies import PPOPolicy class PPO(BaseRLModel): """ Proximal Policy Optimization algorithm (PPO) (clip version) Paper: https://arxiv.org/abs/1707.06347 Code: This implementation borrows code from OpenAI Spinning Up (https://github.com/openai/spinningup/) https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail and and Stable Baselines (PPO2 from https://github.com/hill-a/stable-baselines) Introduction to PPO: https://spinningup.openai.com/en/latest/algorithms/ppo.html :param policy: (PPOPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, ...) :param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str) :param learning_rate: (float or callable) The learning rate, it can be a function of the current progress (from 1 to 0) :param n_steps: (int) The number of steps to run for each environment per update (i.e. batch size is n_steps * n_env where n_env is number of environment copies running in parallel) :param batch_size: (int) Minibatch size :param n_epochs: (int) Number of epoch when optimizing the surrogate loss :param gamma: (float) Discount factor :param gae_lambda: (float) Factor for trade-off of bias vs variance for Generalized Advantage Estimator :param clip_range: (float or callable) Clipping parameter, it can be a function of the current progress (from 1 to 0). :param clip_range_vf: (float or callable) Clipping parameter for the value function, it can be a function of the current progress (from 1 to 0). This is a parameter specific to the OpenAI implementation. If None is passed (default), no clipping will be done on the value function. IMPORTANT: this clipping depends on the reward scaling. :param ent_coef: (float) Entropy coefficient for the loss calculation :param vf_coef: (float) Value function coefficient for the loss calculation :param max_grad_norm: (float) The maximum value for the gradient clipping :param use_sde: (bool) Whether to use State Dependent Exploration (SDE) instead of action noise exploration (default: False) :param sde_sample_freq: (int) Sample a new noise matrix every n steps when using SDE Default: -1 (only sample at the beginning of the rollout) :param target_kl: (float) Limit the KL divergence between updates, because the clipping is not enough to prevent large update see issue #213 (cf https://github.com/hill-a/stable-baselines/issues/213) By default, there is no limit on the kl div. :param tensorboard_log: (str) the log location for tensorboard (if None, no logging) :param create_eval_env: (bool) Whether to create a second environment that will be used for evaluating the agent periodically. (Only available when passing string for the environment) :param policy_kwargs: (dict) additional arguments to be passed to the policy on creation :param verbose: (int) the verbosity level: 0 no output, 1 info, 2 debug :param seed: (int) Seed for the pseudo random generators :param device: (str or th.device) Device (cpu, cuda, ...) on which the code should be run. Setting it to auto, the code will be run on the GPU if possible. :param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance """ def __init__(self, policy: Union[str, Type[PPOPolicy]], env: Union[GymEnv, str], learning_rate: Union[float, Callable] = 3e-4, n_steps: int = 2048, batch_size: Optional[int] = 64, n_epochs: int = 10, gamma: float = 0.99, gae_lambda: float = 0.95, clip_range: float = 0.2, clip_range_vf: Optional[float] = None, ent_coef: float = 0.0, vf_coef: float = 0.5, max_grad_norm: float = 0.5, use_sde: bool = False, sde_sample_freq: int = -1, target_kl: Optional[float] = None, tensorboard_log: Optional[str] = None, create_eval_env: bool = False, policy_kwargs: Optional[Dict[str, Any]] = None, verbose: int = 0, seed: Optional[int] = None, device: Union[th.device, str] = 'auto', _init_setup_model: bool = True): super(PPO, self).__init__(policy, env, PPOPolicy, learning_rate, policy_kwargs=policy_kwargs, verbose=verbose, device=device, use_sde=use_sde, sde_sample_freq=sde_sample_freq, create_eval_env=create_eval_env, support_multi_env=True, seed=seed) self.batch_size = batch_size self.n_epochs = n_epochs self.n_steps = n_steps self.gamma = gamma self.gae_lambda = gae_lambda self.clip_range = clip_range self.clip_range_vf = clip_range_vf self.ent_coef = ent_coef self.vf_coef = vf_coef self.max_grad_norm = max_grad_norm self.rollout_buffer = None self.target_kl = target_kl self.tensorboard_log = tensorboard_log self.tb_writer = None if _init_setup_model: self._setup_model() def _setup_model(self) -> None: self._setup_lr_schedule() self.set_random_seed(self.seed) self.rollout_buffer = RolloutBuffer(self.n_steps, self.observation_space, self.action_space, self.device, gamma=self.gamma, gae_lambda=self.gae_lambda, n_envs=self.n_envs) self.policy = self.policy_class(self.observation_space, self.action_space, self.lr_schedule, use_sde=self.use_sde, device=self.device, **self.policy_kwargs) self.policy = self.policy.to(self.device) self.clip_range = get_schedule_fn(self.clip_range) if self.clip_range_vf is not None: if isinstance(self.clip_range_vf, (float, int)): assert self.clip_range_vf > 0, ('`clip_range_vf` must be positive, ' 'pass `None` to deactivate vf clipping') self.clip_range_vf = get_schedule_fn(self.clip_range_vf) def collect_rollouts(self, env: VecEnv, callback: BaseCallback, rollout_buffer: RolloutBuffer, n_rollout_steps: int = 256) -> bool: assert self._last_obs is not None, "No previous observation was provided" n_steps = 0 rollout_buffer.reset() # Sample new weights for the state dependent exploration if self.use_sde: self.policy.reset_noise(env.num_envs) callback.on_rollout_start() while n_steps < n_rollout_steps: if self.use_sde and self.sde_sample_freq > 0 and n_steps % self.sde_sample_freq == 0: # Sample a new noise matrix self.policy.reset_noise(env.num_envs) with th.no_grad(): # Convert to pytorch tensor obs_tensor = th.as_tensor(self._last_obs).to(self.device) actions, values, log_probs = self.policy.forward(obs_tensor) actions = actions.cpu().numpy() # Rescale and perform action clipped_actions = actions # Clip the actions to avoid out of bound error if isinstance(self.action_space, gym.spaces.Box): clipped_actions = np.clip(actions, self.action_space.low, self.action_space.high) new_obs, rewards, dones, infos = env.step(clipped_actions) if callback.on_step() is False: return False self._update_info_buffer(infos) n_steps += 1 self.num_timesteps += env.num_envs if isinstance(self.action_space, gym.spaces.Discrete): # Reshape in case of discrete action actions = actions.reshape(-1, 1) rollout_buffer.add(self._last_obs, actions, rewards, dones, values, log_probs) self._last_obs = new_obs rollout_buffer.compute_returns_and_advantage(values, dones=dones) callback.on_rollout_end() return True def train(self, n_epochs: int, batch_size: int = 64) -> None: # Update optimizer learning rate self._update_learning_rate(self.policy.optimizer) # Compute current clip range clip_range = self.clip_range(self._current_progress) # Optional: clip range for the value function if self.clip_range_vf is not None: clip_range_vf = self.clip_range_vf(self._current_progress) entropy_losses, all_kl_divs = [], [] pg_losses, value_losses = [], [] clip_fractions = [] # train for gradient_steps epochs for epoch in range(n_epochs): approx_kl_divs = [] # Do a complete pass on the rollout buffer for rollout_data in self.rollout_buffer.get(batch_size): actions = rollout_data.actions if isinstance(self.action_space, spaces.Discrete): # Convert discrete action from float to long actions = rollout_data.actions.long().flatten() # Re-sample the noise matrix because the log_std has changed # TODO: investigate why there is no issue with the gradient # if that line is commented (as in SAC) if self.use_sde: self.policy.reset_noise(batch_size) values, log_prob, entropy = self.policy.evaluate_actions(rollout_data.observations, actions) values = values.flatten() # Normalize advantage advantages = rollout_data.advantages advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8) # ratio between old and new policy, should be one at the first iteration ratio = th.exp(log_prob - rollout_data.old_log_prob) # clipped surrogate loss policy_loss_1 = advantages * ratio policy_loss_2 = advantages * th.clamp(ratio, 1 - clip_range, 1 + clip_range) policy_loss = -th.min(policy_loss_1, policy_loss_2).mean() # Logging pg_losses.append(policy_loss.item()) clip_fraction = th.mean((th.abs(ratio - 1) > clip_range).float()).item() clip_fractions.append(clip_fraction) if self.clip_range_vf is None: # No clipping values_pred = values else: # Clip the different between old and new value # NOTE: this depends on the reward scaling values_pred = rollout_data.old_values + th.clamp(values - rollout_data.old_values, -clip_range_vf, clip_range_vf) # Value loss using the TD(gae_lambda) target value_loss = F.mse_loss(rollout_data.returns, values_pred) value_losses.append(value_loss.item()) # Entropy loss favor exploration if entropy is None: # Approximate entropy when no analytical form entropy_loss = -log_prob.mean() else: entropy_loss = -th.mean(entropy) entropy_losses.append(entropy_loss.item()) loss = policy_loss + self.ent_coef * entropy_loss + self.vf_coef * value_loss # Optimization step self.policy.optimizer.zero_grad() loss.backward() # Clip grad norm th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm) self.policy.optimizer.step() approx_kl_divs.append(th.mean(rollout_data.old_log_prob - log_prob).detach().cpu().numpy()) all_kl_divs.append(np.mean(approx_kl_divs)) if self.target_kl is not None and np.mean(approx_kl_divs) > 1.5 * self.target_kl: print(f"Early stopping at step {epoch} due to reaching max kl: {np.mean(approx_kl_divs):.2f}") break self._n_updates += n_epochs explained_var = explained_variance(self.rollout_buffer.returns.flatten(), self.rollout_buffer.values.flatten()) logger.logkv("n_updates", self._n_updates) logger.logkv("clip_fraction", np.mean(clip_fraction)) logger.logkv("clip_range", clip_range) if self.clip_range_vf is not None: logger.logkv("clip_range_vf", clip_range_vf) logger.logkv("approx_kl", np.mean(approx_kl_divs)) logger.logkv("explained_variance", explained_var) logger.logkv("entropy_loss", np.mean(entropy_losses)) logger.logkv("policy_gradient_loss", np.mean(pg_losses)) logger.logkv("value_loss", np.mean(value_losses)) if hasattr(self.policy, 'log_std'): logger.logkv("std", th.exp(self.policy.log_std).mean().item()) def learn(self, total_timesteps: int, callback: MaybeCallback = None, log_interval: int = 1, eval_env: Optional[GymEnv] = None, eval_freq: int = -1, n_eval_episodes: int = 5, tb_log_name: str = "PPO", eval_log_path: Optional[str] = None, reset_num_timesteps: bool = True) -> 'PPO': iteration = 0 callback = self._setup_learn(eval_env, callback, eval_freq, n_eval_episodes, eval_log_path, reset_num_timesteps) # if self.tensorboard_log is not None and SummaryWriter is not None: # self.tb_writer = SummaryWriter(log_dir=os.path.join(self.tensorboard_log, tb_log_name)) callback.on_training_start(locals(), globals()) while self.num_timesteps < total_timesteps: continue_training = self.collect_rollouts(self.env, callback, self.rollout_buffer, n_rollout_steps=self.n_steps) if continue_training is False: break iteration += 1 self._update_current_progress(self.num_timesteps, total_timesteps) # Display training infos if self.verbose >= 1 and log_interval is not None and iteration % log_interval == 0: fps = int(self.num_timesteps / (time.time() - self.start_time)) logger.logkv("iterations", iteration) if len(self.ep_info_buffer) > 0 and len(self.ep_info_buffer[0]) > 0: logger.logkv('ep_rew_mean', self.safe_mean([ep_info['r'] for ep_info in self.ep_info_buffer])) logger.logkv('ep_len_mean', self.safe_mean([ep_info['l'] for ep_info in self.ep_info_buffer])) logger.logkv("fps", fps) logger.logkv('time_elapsed', int(time.time() - self.start_time)) logger.logkv("total timesteps", self.num_timesteps) logger.dumpkvs() self.train(self.n_epochs, batch_size=self.batch_size) # For tensorboard integration # if self.tb_writer is not None: # self.tb_writer.add_scalar('Eval/reward', mean_reward, self.num_timesteps) callback.on_training_end() return self def get_torch_variables(self) -> Tuple[List[str], List[str]]: """ cf base class """ state_dicts = ["policy", "policy.optimizer"] return state_dicts, []