Source code for sb3_contrib.ppo_recurrent.ppo_recurrent

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

import numpy as np
import torch as th
from gym 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, safe_mean
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 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: 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, 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, 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, # pytype:disable=not-instantiable ) 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 callback.on_step() is False: return False self._update_info_buffer(infos) 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: iteration = 0 total_timesteps, callback = self._setup_learn( total_timesteps, callback, reset_num_timesteps, tb_log_name, progress_bar, ) 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_remaining(self.num_timesteps, total_timesteps) # Display training infos if log_interval is not None and iteration % log_interval == 0: time_elapsed = max((time.time_ns() - self.start_time) / 1e9, sys.float_info.epsilon) fps = int((self.num_timesteps - self._num_timesteps_at_start) / time_elapsed) self.logger.record("time/iterations", iteration, exclude="tensorboard") if len(self.ep_info_buffer) > 0 and len(self.ep_info_buffer[0]) > 0: self.logger.record("rollout/ep_rew_mean", safe_mean([ep_info["r"] for ep_info in self.ep_info_buffer])) self.logger.record("rollout/ep_len_mean", safe_mean([ep_info["l"] for ep_info in self.ep_info_buffer])) self.logger.record("time/fps", fps) self.logger.record("time/time_elapsed", int(time_elapsed), exclude="tensorboard") self.logger.record("time/total_timesteps", self.num_timesteps, exclude="tensorboard") self.logger.dump(step=self.num_timesteps) self.train() callback.on_training_end() return self