Source code for sb3_contrib.ppo_recurrent.ppo_recurrent

from copy import deepcopy
from typing import Any, ClassVar, Dict, Optional, Type, TypeVar, Union

import numpy as np
import torch as th
from gymnasium import spaces
from stable_baselines3.common.buffers import RolloutBuffer
from stable_baselines3.common.callbacks import BaseCallback
from stable_baselines3.common.on_policy_algorithm import OnPolicyAlgorithm
from stable_baselines3.common.policies import BasePolicy
from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
from stable_baselines3.common.utils import explained_variance, get_schedule_fn, obs_as_tensor
from stable_baselines3.common.vec_env import VecEnv

from sb3_contrib.common.recurrent.buffers import RecurrentDictRolloutBuffer, RecurrentRolloutBuffer
from sb3_contrib.common.recurrent.policies import RecurrentActorCriticPolicy
from sb3_contrib.common.recurrent.type_aliases import RNNStates
from sb3_contrib.ppo_recurrent.policies import CnnLstmPolicy, MlpLstmPolicy, MultiInputLstmPolicy

SelfRecurrentPPO = TypeVar("SelfRecurrentPPO", bound="RecurrentPPO")

[docs]class RecurrentPPO(OnPolicyAlgorithm): """ Proximal Policy Optimization algorithm (PPO) (clip version) with support for recurrent policies (LSTM). Based on the original Stable Baselines 3 implementation. Introduction to PPO: :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...) :param env: The environment to learn from (if registered in Gym, can be str) :param learning_rate: The learning rate, it can be a function of the current progress remaining (from 1 to 0) :param n_steps: 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: Minibatch size :param n_epochs: Number of epoch when optimizing the surrogate loss :param gamma: Discount factor :param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator :param clip_range: Clipping parameter, it can be a function of the current progress remaining (from 1 to 0). :param clip_range_vf: Clipping parameter for the value function, it can be a function of the current progress remaining (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 normalize_advantage: Whether to normalize or not the advantage :param ent_coef: Entropy coefficient for the loss calculation :param vf_coef: Value function coefficient for the loss calculation :param max_grad_norm: The maximum value for the gradient clipping :param target_kl: Limit the KL divergence between updates, because the clipping is not enough to prevent large update see issue #213 (cf By default, there is no limit on the kl div. :param stats_window_size: Window size for the rollout logging, specifying the number of episodes to average the reported success rate, mean episode length, and mean reward over :param tensorboard_log: the log location for tensorboard (if None, no logging) :param policy_kwargs: additional arguments to be passed to the policy on creation :param verbose: the verbosity level: 0 no output, 1 info, 2 debug :param seed: Seed for the pseudo random generators :param 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: Whether or not to build the network at the creation of the instance """ policy_aliases: ClassVar[Dict[str, Type[BasePolicy]]] = { "MlpLstmPolicy": MlpLstmPolicy, "CnnLstmPolicy": CnnLstmPolicy, "MultiInputLstmPolicy": MultiInputLstmPolicy, } def __init__( self, policy: Union[str, Type[RecurrentActorCriticPolicy]], env: Union[GymEnv, str], learning_rate: Union[float, Schedule] = 3e-4, n_steps: int = 128, batch_size: Optional[int] = 128, n_epochs: int = 10, gamma: float = 0.99, gae_lambda: float = 0.95, clip_range: Union[float, Schedule] = 0.2, clip_range_vf: Union[None, float, Schedule] = None, normalize_advantage: bool = True, 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, stats_window_size: int = 100, tensorboard_log: Optional[str] = None, 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().__init__( policy, env, learning_rate=learning_rate, n_steps=n_steps, gamma=gamma, gae_lambda=gae_lambda, ent_coef=ent_coef, vf_coef=vf_coef, max_grad_norm=max_grad_norm, use_sde=use_sde, sde_sample_freq=sde_sample_freq, stats_window_size=stats_window_size, tensorboard_log=tensorboard_log, policy_kwargs=policy_kwargs, verbose=verbose, seed=seed, device=device, _init_setup_model=False, supported_action_spaces=( spaces.Box, spaces.Discrete, spaces.MultiDiscrete, spaces.MultiBinary, ), ) self.batch_size = batch_size self.n_epochs = n_epochs self.clip_range = clip_range self.clip_range_vf = clip_range_vf self.normalize_advantage = normalize_advantage self.target_kl = target_kl self._last_lstm_states = None if _init_setup_model: self._setup_model() def _setup_model(self) -> None: self._setup_lr_schedule() self.set_random_seed(self.seed) buffer_cls = RecurrentDictRolloutBuffer if isinstance(self.observation_space, spaces.Dict) else RecurrentRolloutBuffer self.policy = self.policy_class( self.observation_space, self.action_space, self.lr_schedule, use_sde=self.use_sde, **self.policy_kwargs, ) self.policy = # We assume that LSTM for the actor and the critic # have the same architecture lstm = self.policy.lstm_actor if not isinstance(self.policy, RecurrentActorCriticPolicy): raise ValueError("Policy must subclass RecurrentActorCriticPolicy") single_hidden_state_shape = (lstm.num_layers, self.n_envs, lstm.hidden_size) # hidden and cell states for actor and critic self._last_lstm_states = RNNStates( ( th.zeros(single_hidden_state_shape, device=self.device), th.zeros(single_hidden_state_shape, device=self.device), ), ( th.zeros(single_hidden_state_shape, device=self.device), th.zeros(single_hidden_state_shape, device=self.device), ), ) hidden_state_buffer_shape = (self.n_steps, lstm.num_layers, self.n_envs, lstm.hidden_size) self.rollout_buffer = buffer_cls( self.n_steps, self.observation_space, self.action_space, hidden_state_buffer_shape, self.device, gamma=self.gamma, gae_lambda=self.gae_lambda, n_envs=self.n_envs, ) # Initialize schedules for policy/value clipping 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)
[docs] def collect_rollouts( self, env: VecEnv, callback: BaseCallback, rollout_buffer: RolloutBuffer, n_rollout_steps: int, ) -> bool: """ Collect experiences using the current policy and fill a ``RolloutBuffer``. The term rollout here refers to the model-free notion and should not be used with the concept of rollout used in model-based RL or planning. :param env: The training environment :param callback: Callback that will be called at each step (and at the beginning and end of the rollout) :param rollout_buffer: Buffer to fill with rollouts :param n_steps: Number of experiences to collect per environment :return: True if function returned with at least `n_rollout_steps` collected, False if callback terminated rollout prematurely. """ assert isinstance( rollout_buffer, (RecurrentRolloutBuffer, RecurrentDictRolloutBuffer) ), f"{rollout_buffer} doesn't support recurrent policy" assert self._last_obs is not None, "No previous observation was provided" # Switch to eval mode (this affects batch norm / dropout) self.policy.set_training_mode(False) 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() lstm_states = deepcopy(self._last_lstm_states) 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 or to TensorDict obs_tensor = obs_as_tensor(self._last_obs, self.device) episode_starts = th.tensor(self._last_episode_starts, dtype=th.float32, device=self.device) actions, values, log_probs, lstm_states = self.policy.forward(obs_tensor, lstm_states, episode_starts) 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, spaces.Box): clipped_actions = np.clip(actions, self.action_space.low, self.action_space.high) new_obs, rewards, dones, infos = env.step(clipped_actions) self.num_timesteps += env.num_envs # Give access to local variables callback.update_locals(locals()) if not callback.on_step(): return False self._update_info_buffer(infos, dones) n_steps += 1 if isinstance(self.action_space, spaces.Discrete): # Reshape in case of discrete action actions = actions.reshape(-1, 1) # Handle timeout by bootstraping with value function # see GitHub issue #633 for idx, done_ in enumerate(dones): if ( done_ and infos[idx].get("terminal_observation") is not None and infos[idx].get("TimeLimit.truncated", False) ): terminal_obs = self.policy.obs_to_tensor(infos[idx]["terminal_observation"])[0] with th.no_grad(): terminal_lstm_state = ( lstm_states.vf[0][:, idx : idx + 1, :].contiguous(), lstm_states.vf[1][:, idx : idx + 1, :].contiguous(), ) # terminal_lstm_state = None episode_starts = th.tensor([False], dtype=th.float32, device=self.device) terminal_value = self.policy.predict_values(terminal_obs, terminal_lstm_state, episode_starts)[0] rewards[idx] += self.gamma * terminal_value rollout_buffer.add( self._last_obs, actions, rewards, self._last_episode_starts, values, log_probs, lstm_states=self._last_lstm_states, ) self._last_obs = new_obs self._last_episode_starts = dones self._last_lstm_states = lstm_states with th.no_grad(): # Compute value for the last timestep episode_starts = th.tensor(dones, dtype=th.float32, device=self.device) values = self.policy.predict_values(obs_as_tensor(new_obs, self.device), lstm_states.vf, episode_starts) rollout_buffer.compute_returns_and_advantage(last_values=values, dones=dones) callback.on_rollout_end() return True
[docs] def train(self) -> None: """ Update policy using the currently gathered rollout buffer. """ # Switch to train mode (this affects batch norm / dropout) self.policy.set_training_mode(True) # Update optimizer learning rate self._update_learning_rate(self.policy.optimizer) # Compute current clip range clip_range = self.clip_range(self._current_progress_remaining) # 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_remaining) entropy_losses = [] pg_losses, value_losses = [], [] clip_fractions = [] continue_training = True # train for n_epochs epochs for epoch in range(self.n_epochs): approx_kl_divs = [] # Do a complete pass on the rollout buffer for rollout_data in self.rollout_buffer.get(self.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() # Convert mask from float to bool mask = rollout_data.mask > 1e-8 # Re-sample the noise matrix because the log_std has changed if self.use_sde: self.policy.reset_noise(self.batch_size) values, log_prob, entropy = self.policy.evaluate_actions( rollout_data.observations, actions, rollout_data.lstm_states, rollout_data.episode_starts, ) values = values.flatten() # Normalize advantage advantages = rollout_data.advantages if self.normalize_advantage: advantages = (advantages - advantages[mask].mean()) / (advantages[mask].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.mean(th.min(policy_loss_1, policy_loss_2)[mask]) # Logging pg_losses.append(policy_loss.item()) clip_fraction = th.mean((th.abs(ratio - 1) > clip_range).float()[mask]).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 # Mask padded sequences value_loss = th.mean(((rollout_data.returns - values_pred) ** 2)[mask]) value_losses.append(value_loss.item()) # Entropy loss favor exploration if entropy is None: # Approximate entropy when no analytical form entropy_loss = -th.mean(-log_prob[mask]) else: entropy_loss = -th.mean(entropy[mask]) entropy_losses.append(entropy_loss.item()) loss = policy_loss + self.ent_coef * entropy_loss + self.vf_coef * value_loss # Calculate approximate form of reverse KL Divergence for early stopping # see issue #417: # and discussion in PR #419: # and Schulman blog: with th.no_grad(): log_ratio = log_prob - rollout_data.old_log_prob approx_kl_div = th.mean(((th.exp(log_ratio) - 1) - log_ratio)[mask]).cpu().numpy() approx_kl_divs.append(approx_kl_div) if self.target_kl is not None and approx_kl_div > 1.5 * self.target_kl: continue_training = False if self.verbose >= 1: print(f"Early stopping at step {epoch} due to reaching max kl: {approx_kl_div:.2f}") break # 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() if not continue_training: break self._n_updates += self.n_epochs explained_var = explained_variance(self.rollout_buffer.values.flatten(), self.rollout_buffer.returns.flatten()) # Logs self.logger.record("train/entropy_loss", np.mean(entropy_losses)) self.logger.record("train/policy_gradient_loss", np.mean(pg_losses)) self.logger.record("train/value_loss", np.mean(value_losses)) self.logger.record("train/approx_kl", np.mean(approx_kl_divs)) self.logger.record("train/clip_fraction", np.mean(clip_fractions)) self.logger.record("train/loss", loss.item()) self.logger.record("train/explained_variance", explained_var) if hasattr(self.policy, "log_std"): self.logger.record("train/std", th.exp(self.policy.log_std).mean().item()) self.logger.record("train/n_updates", self._n_updates, exclude="tensorboard") self.logger.record("train/clip_range", clip_range) if self.clip_range_vf is not None: self.logger.record("train/clip_range_vf", clip_range_vf)
[docs] def learn( self: SelfRecurrentPPO, total_timesteps: int, callback: MaybeCallback = None, log_interval: int = 1, tb_log_name: str = "RecurrentPPO", reset_num_timesteps: bool = True, progress_bar: bool = False, ) -> SelfRecurrentPPO: return super().learn( total_timesteps=total_timesteps, callback=callback, log_interval=log_interval, tb_log_name=tb_log_name, reset_num_timesteps=reset_num_timesteps, progress_bar=progress_bar, )