538 lines
23 KiB
Python
538 lines
23 KiB
Python
import sys
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import time
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from copy import deepcopy
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from typing import Any, Dict, Optional, Tuple, Type, Union
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import gym
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import numpy as np
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import torch as th
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from gym import spaces
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from stable_baselines3.common.buffers import RolloutBuffer
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from stable_baselines3.common.callbacks import BaseCallback
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from stable_baselines3.common.on_policy_algorithm import OnPolicyAlgorithm
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from stable_baselines3.common.policies import BasePolicy
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from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
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from stable_baselines3.common.utils import explained_variance, get_schedule_fn, obs_as_tensor, safe_mean
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from stable_baselines3.common.vec_env import VecEnv
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from sb3_contrib.common.recurrent.buffers import RecurrentDictRolloutBuffer, RecurrentRolloutBuffer
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from sb3_contrib.common.recurrent.policies import RecurrentActorCriticPolicy
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from sb3_contrib.common.recurrent.type_aliases import RNNStates
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from sb3_contrib.ppo_recurrent.policies import CnnLstmPolicy, MlpLstmPolicy, MultiInputLstmPolicy
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class RecurrentPPO(OnPolicyAlgorithm):
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"""
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Proximal Policy Optimization algorithm (PPO) (clip version)
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with support for recurrent policies (LSTM).
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Based on the original Stable Baselines 3 implementation.
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Introduction to PPO: https://spinningup.openai.com/en/latest/algorithms/ppo.html
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:param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
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:param env: The environment to learn from (if registered in Gym, can be str)
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:param learning_rate: The learning rate, it can be a function
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of the current progress remaining (from 1 to 0)
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:param n_steps: The number of steps to run for each environment per update
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(i.e. batch size is n_steps * n_env where n_env is number of environment copies running in parallel)
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:param batch_size: Minibatch size
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:param n_epochs: Number of epoch when optimizing the surrogate loss
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:param gamma: Discount factor
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:param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator
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:param clip_range: Clipping parameter, it can be a function of the current progress
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remaining (from 1 to 0).
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:param clip_range_vf: Clipping parameter for the value function,
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it can be a function of the current progress remaining (from 1 to 0).
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This is a parameter specific to the OpenAI implementation. If None is passed (default),
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no clipping will be done on the value function.
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IMPORTANT: this clipping depends on the reward scaling.
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:param normalize_advantage: Whether to normalize or not the advantage
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:param ent_coef: Entropy coefficient for the loss calculation
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:param vf_coef: Value function coefficient for the loss calculation
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:param max_grad_norm: The maximum value for the gradient clipping
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:param target_kl: Limit the KL divergence between updates,
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because the clipping is not enough to prevent large update
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see issue #213 (cf https://github.com/hill-a/stable-baselines/issues/213)
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By default, there is no limit on the kl div.
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:param tensorboard_log: the log location for tensorboard (if None, no logging)
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:param create_eval_env: Whether to create a second environment that will be
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used for evaluating the agent periodically (Only available when passing string for the environment).
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Caution, this parameter is deprecated and will be removed in the future.
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:param policy_kwargs: additional arguments to be passed to the policy on creation
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:param verbose: the verbosity level: 0 no output, 1 info, 2 debug
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:param seed: Seed for the pseudo random generators
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:param device: Device (cpu, cuda, ...) on which the code should be run.
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Setting it to auto, the code will be run on the GPU if possible.
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:param _init_setup_model: Whether or not to build the network at the creation of the instance
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"""
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policy_aliases: Dict[str, Type[BasePolicy]] = {
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"MlpLstmPolicy": MlpLstmPolicy,
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"CnnLstmPolicy": CnnLstmPolicy,
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"MultiInputLstmPolicy": MultiInputLstmPolicy,
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}
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def __init__(
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self,
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policy: Union[str, Type[RecurrentActorCriticPolicy]],
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env: Union[GymEnv, str],
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learning_rate: Union[float, Schedule] = 3e-4,
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n_steps: int = 128,
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batch_size: Optional[int] = 128,
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n_epochs: int = 10,
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gamma: float = 0.99,
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gae_lambda: float = 0.95,
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clip_range: Union[float, Schedule] = 0.2,
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clip_range_vf: Union[None, float, Schedule] = None,
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normalize_advantage: bool = True,
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ent_coef: float = 0.0,
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vf_coef: float = 0.5,
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max_grad_norm: float = 0.5,
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use_sde: bool = False,
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sde_sample_freq: int = -1,
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target_kl: Optional[float] = None,
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tensorboard_log: Optional[str] = None,
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create_eval_env: bool = False,
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policy_kwargs: Optional[Dict[str, Any]] = None,
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verbose: int = 0,
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seed: Optional[int] = None,
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device: Union[th.device, str] = "auto",
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_init_setup_model: bool = True,
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):
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super().__init__(
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policy,
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env,
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learning_rate=learning_rate,
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n_steps=n_steps,
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gamma=gamma,
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gae_lambda=gae_lambda,
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ent_coef=ent_coef,
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vf_coef=vf_coef,
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max_grad_norm=max_grad_norm,
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use_sde=use_sde,
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sde_sample_freq=sde_sample_freq,
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tensorboard_log=tensorboard_log,
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create_eval_env=create_eval_env,
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policy_kwargs=policy_kwargs,
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verbose=verbose,
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seed=seed,
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device=device,
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_init_setup_model=False,
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supported_action_spaces=(
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spaces.Box,
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spaces.Discrete,
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spaces.MultiDiscrete,
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spaces.MultiBinary,
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),
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)
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self.batch_size = batch_size
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self.n_epochs = n_epochs
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self.clip_range = clip_range
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self.clip_range_vf = clip_range_vf
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self.normalize_advantage = normalize_advantage
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self.target_kl = target_kl
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self._last_lstm_states = None
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if _init_setup_model:
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self._setup_model()
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def _setup_model(self) -> None:
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self._setup_lr_schedule()
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self.set_random_seed(self.seed)
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buffer_cls = (
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RecurrentDictRolloutBuffer if isinstance(self.observation_space, gym.spaces.Dict) else RecurrentRolloutBuffer
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)
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self.policy = self.policy_class(
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self.observation_space,
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self.action_space,
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self.lr_schedule,
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use_sde=self.use_sde,
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**self.policy_kwargs, # pytype:disable=not-instantiable
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)
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self.policy = self.policy.to(self.device)
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# We assume that LSTM for the actor and the critic
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# have the same architecture
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lstm = self.policy.lstm_actor
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if not isinstance(self.policy, RecurrentActorCriticPolicy):
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raise ValueError("Policy must subclass RecurrentActorCriticPolicy")
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single_hidden_state_shape = (lstm.num_layers, self.n_envs, lstm.hidden_size)
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# hidden and cell states for actor and critic
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self._last_lstm_states = RNNStates(
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(
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th.zeros(single_hidden_state_shape).to(self.device),
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th.zeros(single_hidden_state_shape).to(self.device),
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),
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(
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th.zeros(single_hidden_state_shape).to(self.device),
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th.zeros(single_hidden_state_shape).to(self.device),
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),
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)
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hidden_state_buffer_shape = (self.n_steps, lstm.num_layers, self.n_envs, lstm.hidden_size)
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self.rollout_buffer = buffer_cls(
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self.n_steps,
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self.observation_space,
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self.action_space,
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hidden_state_buffer_shape,
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self.device,
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gamma=self.gamma,
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gae_lambda=self.gae_lambda,
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n_envs=self.n_envs,
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)
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# Initialize schedules for policy/value clipping
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self.clip_range = get_schedule_fn(self.clip_range)
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if self.clip_range_vf is not None:
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if isinstance(self.clip_range_vf, (float, int)):
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assert self.clip_range_vf > 0, "`clip_range_vf` must be positive, pass `None` to deactivate vf clipping"
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self.clip_range_vf = get_schedule_fn(self.clip_range_vf)
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def _setup_learn(
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self,
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total_timesteps: int,
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eval_env: Optional[GymEnv],
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callback: MaybeCallback = None,
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eval_freq: int = 10000,
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n_eval_episodes: int = 5,
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log_path: Optional[str] = None,
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reset_num_timesteps: bool = True,
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tb_log_name: str = "RecurrentPPO",
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) -> Tuple[int, BaseCallback]:
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"""
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Initialize different variables needed for training.
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:param total_timesteps: The total number of samples (env steps) to train on
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:param eval_env: Environment to use for evaluation.
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Caution, this parameter is deprecated and will be removed in the future.
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Please use `EvalCallback` or a custom Callback instead.
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:param callback: Callback(s) called at every step with state of the algorithm.
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:param eval_freq: Evaluate the agent every ``eval_freq`` timesteps (this may vary a little).
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Caution, this parameter is deprecated and will be removed in the future.
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Please use `EvalCallback` or a custom Callback instead.
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:param n_eval_episodes: How many episodes to play per evaluation
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:param log_path: Path to a folder where the evaluations will be saved
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:param reset_num_timesteps: Whether to reset or not the ``num_timesteps`` attribute
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:param tb_log_name: the name of the run for tensorboard log
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:return:
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"""
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total_timesteps, callback = super()._setup_learn(
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total_timesteps,
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eval_env,
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callback,
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eval_freq,
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n_eval_episodes,
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log_path,
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reset_num_timesteps,
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tb_log_name,
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)
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return total_timesteps, callback
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def collect_rollouts(
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self,
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env: VecEnv,
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callback: BaseCallback,
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rollout_buffer: RolloutBuffer,
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n_rollout_steps: int,
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) -> bool:
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"""
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Collect experiences using the current policy and fill a ``RolloutBuffer``.
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The term rollout here refers to the model-free notion and should not
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be used with the concept of rollout used in model-based RL or planning.
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:param env: The training environment
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:param callback: Callback that will be called at each step
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(and at the beginning and end of the rollout)
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:param rollout_buffer: Buffer to fill with rollouts
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:param n_steps: Number of experiences to collect per environment
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:return: True if function returned with at least `n_rollout_steps`
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collected, False if callback terminated rollout prematurely.
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"""
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assert isinstance(
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rollout_buffer, (RecurrentRolloutBuffer, RecurrentDictRolloutBuffer)
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), f"{rollout_buffer} doesn't support recurrent policy"
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assert self._last_obs is not None, "No previous observation was provided"
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# Switch to eval mode (this affects batch norm / dropout)
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self.policy.set_training_mode(False)
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n_steps = 0
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rollout_buffer.reset()
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# Sample new weights for the state dependent exploration
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if self.use_sde:
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self.policy.reset_noise(env.num_envs)
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callback.on_rollout_start()
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lstm_states = deepcopy(self._last_lstm_states)
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while n_steps < n_rollout_steps:
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if self.use_sde and self.sde_sample_freq > 0 and n_steps % self.sde_sample_freq == 0:
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# Sample a new noise matrix
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self.policy.reset_noise(env.num_envs)
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with th.no_grad():
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# Convert to pytorch tensor or to TensorDict
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obs_tensor = obs_as_tensor(self._last_obs, self.device)
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episode_starts = th.tensor(self._last_episode_starts).float().to(self.device)
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actions, values, log_probs, lstm_states = self.policy.forward(obs_tensor, lstm_states, episode_starts)
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actions = actions.cpu().numpy()
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# Rescale and perform action
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clipped_actions = actions
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# Clip the actions to avoid out of bound error
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if isinstance(self.action_space, gym.spaces.Box):
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clipped_actions = np.clip(actions, self.action_space.low, self.action_space.high)
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new_obs, rewards, dones, infos = env.step(clipped_actions)
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self.num_timesteps += env.num_envs
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# Give access to local variables
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callback.update_locals(locals())
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if callback.on_step() is False:
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return False
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self._update_info_buffer(infos)
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n_steps += 1
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if isinstance(self.action_space, gym.spaces.Discrete):
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# Reshape in case of discrete action
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actions = actions.reshape(-1, 1)
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# Handle timeout by bootstraping with value function
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# see GitHub issue #633
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for idx, done_ in enumerate(dones):
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if (
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done_
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and infos[idx].get("terminal_observation") is not None
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and infos[idx].get("TimeLimit.truncated", False)
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):
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terminal_obs = self.policy.obs_to_tensor(infos[idx]["terminal_observation"])[0]
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with th.no_grad():
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terminal_lstm_state = (
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lstm_states.vf[0][:, idx : idx + 1, :],
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lstm_states.vf[1][:, idx : idx + 1, :],
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)
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# terminal_lstm_state = None
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episode_starts = th.tensor([False]).float().to(self.device)
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terminal_value = self.policy.predict_values(terminal_obs, terminal_lstm_state, episode_starts)[0]
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rewards[idx] += self.gamma * terminal_value
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rollout_buffer.add(
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self._last_obs,
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actions,
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rewards,
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self._last_episode_starts,
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values,
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log_probs,
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lstm_states=self._last_lstm_states,
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)
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self._last_obs = new_obs
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self._last_episode_starts = dones
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self._last_lstm_states = lstm_states
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with th.no_grad():
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# Compute value for the last timestep
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episode_starts = th.tensor(dones).float().to(self.device)
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values = self.policy.predict_values(obs_as_tensor(new_obs, self.device), lstm_states.vf, episode_starts)
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rollout_buffer.compute_returns_and_advantage(last_values=values, dones=dones)
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callback.on_rollout_end()
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return True
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def train(self) -> None:
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"""
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Update policy using the currently gathered rollout buffer.
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"""
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# Switch to train mode (this affects batch norm / dropout)
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self.policy.set_training_mode(True)
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# Update optimizer learning rate
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self._update_learning_rate(self.policy.optimizer)
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# Compute current clip range
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clip_range = self.clip_range(self._current_progress_remaining)
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# Optional: clip range for the value function
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if self.clip_range_vf is not None:
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clip_range_vf = self.clip_range_vf(self._current_progress_remaining)
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entropy_losses = []
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pg_losses, value_losses = [], []
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clip_fractions = []
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continue_training = True
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# train for n_epochs epochs
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for epoch in range(self.n_epochs):
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approx_kl_divs = []
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# Do a complete pass on the rollout buffer
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for rollout_data in self.rollout_buffer.get(self.batch_size):
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actions = rollout_data.actions
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if isinstance(self.action_space, spaces.Discrete):
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# Convert discrete action from float to long
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actions = rollout_data.actions.long().flatten()
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# Convert mask from float to bool
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mask = rollout_data.mask > 1e-8
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# Re-sample the noise matrix because the log_std has changed
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if self.use_sde:
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self.policy.reset_noise(self.batch_size)
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values, log_prob, entropy = self.policy.evaluate_actions(
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rollout_data.observations,
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actions,
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rollout_data.lstm_states,
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rollout_data.episode_starts,
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)
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values = values.flatten()
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# Normalize advantage
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advantages = rollout_data.advantages
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if self.normalize_advantage:
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advantages = (advantages - advantages[mask].mean()) / (advantages[mask].std() + 1e-8)
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# ratio between old and new policy, should be one at the first iteration
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ratio = th.exp(log_prob - rollout_data.old_log_prob)
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# clipped surrogate loss
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policy_loss_1 = advantages * ratio
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policy_loss_2 = advantages * th.clamp(ratio, 1 - clip_range, 1 + clip_range)
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policy_loss = -th.mean(th.min(policy_loss_1, policy_loss_2)[mask])
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# Logging
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pg_losses.append(policy_loss.item())
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clip_fraction = th.mean((th.abs(ratio - 1) > clip_range).float()[mask]).item()
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clip_fractions.append(clip_fraction)
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if self.clip_range_vf is None:
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# No clipping
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values_pred = values
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else:
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# Clip the different between old and new value
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# NOTE: this depends on the reward scaling
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values_pred = rollout_data.old_values + th.clamp(
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values - rollout_data.old_values, -clip_range_vf, clip_range_vf
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)
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# Value loss using the TD(gae_lambda) target
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# Mask padded sequences
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value_loss = th.mean(((rollout_data.returns - values_pred) ** 2)[mask])
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value_losses.append(value_loss.item())
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# Entropy loss favor exploration
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if entropy is None:
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# Approximate entropy when no analytical form
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entropy_loss = -th.mean(-log_prob[mask])
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else:
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entropy_loss = -th.mean(entropy[mask])
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entropy_losses.append(entropy_loss.item())
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loss = policy_loss + self.ent_coef * entropy_loss + self.vf_coef * value_loss
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# Calculate approximate form of reverse KL Divergence for early stopping
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# see issue #417: https://github.com/DLR-RM/stable-baselines3/issues/417
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# and discussion in PR #419: https://github.com/DLR-RM/stable-baselines3/pull/419
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# and Schulman blog: http://joschu.net/blog/kl-approx.html
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with th.no_grad():
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log_ratio = log_prob - rollout_data.old_log_prob
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approx_kl_div = th.mean(((th.exp(log_ratio) - 1) - log_ratio)[mask]).cpu().numpy()
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approx_kl_divs.append(approx_kl_div)
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if self.target_kl is not None and approx_kl_div > 1.5 * self.target_kl:
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continue_training = False
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if self.verbose >= 1:
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print(f"Early stopping at step {epoch} due to reaching max kl: {approx_kl_div:.2f}")
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break
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# Optimization step
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self.policy.optimizer.zero_grad()
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|
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)
|
|
|
|
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 = "RecurrentPPO",
|
|
eval_log_path: Optional[str] = None,
|
|
reset_num_timesteps: bool = True,
|
|
) -> "RecurrentPPO":
|
|
iteration = 0
|
|
|
|
total_timesteps, callback = self._setup_learn(
|
|
total_timesteps, eval_env, callback, eval_freq, n_eval_episodes, eval_log_path, reset_num_timesteps, 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_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
|