Source code for stable_baselines3.common.on_policy_algorithm

import sys
import time
from typing import Any, Dict, List, Optional, Tuple, Type, TypeVar, Union

import numpy as np
import torch as th
from gymnasium import spaces

from stable_baselines3.common.base_class import BaseAlgorithm
from stable_baselines3.common.buffers import DictRolloutBuffer, RolloutBuffer
from stable_baselines3.common.callbacks import BaseCallback
from stable_baselines3.common.policies import ActorCriticPolicy
from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
from stable_baselines3.common.utils import obs_as_tensor, safe_mean
from stable_baselines3.common.vec_env import VecEnv

SelfOnPolicyAlgorithm = TypeVar("SelfOnPolicyAlgorithm", bound="OnPolicyAlgorithm")

[docs]class OnPolicyAlgorithm(BaseAlgorithm): """ The base for On-Policy algorithms (ex: A2C/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 gamma: Discount factor :param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator. Equivalent to classic advantage when set to 1. :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 use_sde: Whether to use generalized State Dependent Exploration (gSDE) instead of action noise exploration (default: False) :param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE Default: -1 (only sample at the beginning of the rollout) :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 monitor_wrapper: When creating an environment, whether to wrap it or not in a Monitor wrapper. :param policy_kwargs: additional arguments to be passed to the policy on creation :param verbose: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for debug messages :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 :param supported_action_spaces: The action spaces supported by the algorithm. """ rollout_buffer: RolloutBuffer policy: ActorCriticPolicy def __init__( self, policy: Union[str, Type[ActorCriticPolicy]], env: Union[GymEnv, str], learning_rate: Union[float, Schedule], n_steps: int, gamma: float, gae_lambda: float, ent_coef: float, vf_coef: float, max_grad_norm: float, use_sde: bool, sde_sample_freq: int, stats_window_size: int = 100, tensorboard_log: Optional[str] = None, monitor_wrapper: bool = True, 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, supported_action_spaces: Optional[Tuple[Type[spaces.Space], ...]] = None, ): super().__init__( policy=policy, env=env, learning_rate=learning_rate, policy_kwargs=policy_kwargs, verbose=verbose, device=device, use_sde=use_sde, sde_sample_freq=sde_sample_freq, support_multi_env=True, seed=seed, stats_window_size=stats_window_size, tensorboard_log=tensorboard_log, supported_action_spaces=supported_action_spaces, ) self.n_steps = n_steps self.gamma = gamma self.gae_lambda = gae_lambda self.ent_coef = ent_coef self.vf_coef = vf_coef self.max_grad_norm = max_grad_norm if _init_setup_model: self._setup_model() def _setup_model(self) -> None: self._setup_lr_schedule() self.set_random_seed(self.seed) buffer_cls = DictRolloutBuffer if isinstance(self.observation_space, spaces.Dict) else RolloutBuffer self.rollout_buffer = buffer_cls( self.n_steps, self.observation_space, self.action_space, device=self.device, gamma=self.gamma, gae_lambda=self.gae_lambda, n_envs=self.n_envs, ) # pytype:disable=not-instantiable self.policy = self.policy_class( # type: ignore[assignment] self.observation_space, self.action_space, self.lr_schedule, use_sde=self.use_sde, **self.policy_kwargs ) # pytype:enable=not-instantiable self.policy =
[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_rollout_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 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() 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) actions, values, log_probs = self.policy(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, 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_value = self.policy.predict_values(terminal_obs)[0] # type: ignore[arg-type] rewards[idx] += self.gamma * terminal_value rollout_buffer.add( self._last_obs, # type: ignore[arg-type] actions, rewards, self._last_episode_starts, # type: ignore[arg-type] values, log_probs, ) self._last_obs = new_obs # type: ignore[assignment] self._last_episode_starts = dones with th.no_grad(): # Compute value for the last timestep values = self.policy.predict_values(obs_as_tensor(new_obs, self.device)) # type: ignore[arg-type] rollout_buffer.compute_returns_and_advantage(last_values=values, dones=dones) callback.on_rollout_end() return True
[docs] def train(self) -> None: """ Consume current rollout data and update policy parameters. Implemented by individual algorithms. """ raise NotImplementedError
[docs] def learn( self: SelfOnPolicyAlgorithm, total_timesteps: int, callback: MaybeCallback = None, log_interval: int = 1, tb_log_name: str = "OnPolicyAlgorithm", reset_num_timesteps: bool = True, progress_bar: bool = False, ) -> SelfOnPolicyAlgorithm: 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()) assert self.env is not None 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: assert self.ep_info_buffer is not None 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
def _get_torch_save_params(self) -> Tuple[List[str], List[str]]: state_dicts = ["policy", "policy.optimizer"] return state_dicts, []