mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-16 21:10:08 +00:00
82bc63fca4
* apply black * Reformat tests --------- Co-authored-by: Antonin Raffin <antonin.raffin@ensta.org>
692 lines
26 KiB
Python
692 lines
26 KiB
Python
import os
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import warnings
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from abc import ABC, abstractmethod
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from typing import Any, Callable, Dict, List, Optional, Union
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import gym
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import numpy as np
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from stable_baselines3.common.logger import Logger
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try:
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from tqdm import TqdmExperimentalWarning
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# Remove experimental warning
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warnings.filterwarnings("ignore", category=TqdmExperimentalWarning)
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from tqdm.rich import tqdm
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except ImportError:
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# Rich not installed, we only throw an error
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# if the progress bar is used
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tqdm = None
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from stable_baselines3.common import base_class # pytype: disable=pyi-error
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from stable_baselines3.common.evaluation import evaluate_policy
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from stable_baselines3.common.vec_env import DummyVecEnv, VecEnv, sync_envs_normalization
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class BaseCallback(ABC):
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"""
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Base class for callback.
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:param verbose: Verbosity level: 0 for no output, 1 for info messages, 2 for debug messages
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"""
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# The RL model
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# Type hint as string to avoid circular import
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model: "base_class.BaseAlgorithm"
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logger: Logger
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def __init__(self, verbose: int = 0):
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super().__init__()
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# An alias for self.model.get_env(), the environment used for training
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self.training_env = None # type: Union[gym.Env, VecEnv, None]
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# Number of time the callback was called
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self.n_calls = 0 # type: int
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# n_envs * n times env.step() was called
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self.num_timesteps = 0 # type: int
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self.verbose = verbose
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self.locals: Dict[str, Any] = {}
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self.globals: Dict[str, Any] = {}
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# Sometimes, for event callback, it is useful
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# to have access to the parent object
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self.parent = None # type: Optional[BaseCallback]
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# Type hint as string to avoid circular import
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def init_callback(self, model: "base_class.BaseAlgorithm") -> None:
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"""
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Initialize the callback by saving references to the
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RL model and the training environment for convenience.
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"""
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self.model = model
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self.training_env = model.get_env()
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self.logger = model.logger
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self._init_callback()
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def _init_callback(self) -> None:
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pass
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def on_training_start(self, locals_: Dict[str, Any], globals_: Dict[str, Any]) -> None:
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# Those are reference and will be updated automatically
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self.locals = locals_
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self.globals = globals_
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# Update num_timesteps in case training was done before
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self.num_timesteps = self.model.num_timesteps
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self._on_training_start()
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def _on_training_start(self) -> None:
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pass
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def on_rollout_start(self) -> None:
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self._on_rollout_start()
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def _on_rollout_start(self) -> None:
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pass
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@abstractmethod
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def _on_step(self) -> bool:
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"""
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:return: If the callback returns False, training is aborted early.
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"""
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return True
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def on_step(self) -> bool:
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"""
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This method will be called by the model after each call to ``env.step()``.
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For child callback (of an ``EventCallback``), this will be called
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when the event is triggered.
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:return: If the callback returns False, training is aborted early.
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"""
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self.n_calls += 1
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self.num_timesteps = self.model.num_timesteps
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return self._on_step()
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def on_training_end(self) -> None:
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self._on_training_end()
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def _on_training_end(self) -> None:
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pass
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def on_rollout_end(self) -> None:
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self._on_rollout_end()
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def _on_rollout_end(self) -> None:
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pass
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def update_locals(self, locals_: Dict[str, Any]) -> None:
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"""
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Update the references to the local variables.
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:param locals_: the local variables during rollout collection
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"""
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self.locals.update(locals_)
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self.update_child_locals(locals_)
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def update_child_locals(self, locals_: Dict[str, Any]) -> None:
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"""
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Update the references to the local variables on sub callbacks.
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:param locals_: the local variables during rollout collection
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"""
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pass
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class EventCallback(BaseCallback):
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"""
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Base class for triggering callback on event.
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:param callback: Callback that will be called
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when an event is triggered.
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:param verbose: Verbosity level: 0 for no output, 1 for info messages, 2 for debug messages
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"""
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def __init__(self, callback: Optional[BaseCallback] = None, verbose: int = 0):
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super().__init__(verbose=verbose)
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self.callback = callback
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# Give access to the parent
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if callback is not None:
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self.callback.parent = self
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def init_callback(self, model: "base_class.BaseAlgorithm") -> None:
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super().init_callback(model)
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if self.callback is not None:
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self.callback.init_callback(self.model)
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def _on_training_start(self) -> None:
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if self.callback is not None:
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self.callback.on_training_start(self.locals, self.globals)
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def _on_event(self) -> bool:
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if self.callback is not None:
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return self.callback.on_step()
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return True
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def _on_step(self) -> bool:
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return True
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def update_child_locals(self, locals_: Dict[str, Any]) -> None:
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"""
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Update the references to the local variables.
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:param locals_: the local variables during rollout collection
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"""
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if self.callback is not None:
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self.callback.update_locals(locals_)
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class CallbackList(BaseCallback):
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"""
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Class for chaining callbacks.
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:param callbacks: A list of callbacks that will be called
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sequentially.
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"""
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def __init__(self, callbacks: List[BaseCallback]):
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super().__init__()
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assert isinstance(callbacks, list)
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self.callbacks = callbacks
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def _init_callback(self) -> None:
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for callback in self.callbacks:
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callback.init_callback(self.model)
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def _on_training_start(self) -> None:
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for callback in self.callbacks:
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callback.on_training_start(self.locals, self.globals)
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def _on_rollout_start(self) -> None:
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for callback in self.callbacks:
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callback.on_rollout_start()
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def _on_step(self) -> bool:
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continue_training = True
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for callback in self.callbacks:
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# Return False (stop training) if at least one callback returns False
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continue_training = callback.on_step() and continue_training
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return continue_training
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def _on_rollout_end(self) -> None:
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for callback in self.callbacks:
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callback.on_rollout_end()
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def _on_training_end(self) -> None:
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for callback in self.callbacks:
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callback.on_training_end()
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def update_child_locals(self, locals_: Dict[str, Any]) -> None:
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"""
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Update the references to the local variables.
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:param locals_: the local variables during rollout collection
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"""
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for callback in self.callbacks:
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callback.update_locals(locals_)
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class CheckpointCallback(BaseCallback):
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"""
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Callback for saving a model every ``save_freq`` calls
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to ``env.step()``.
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By default, it only saves model checkpoints,
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you need to pass ``save_replay_buffer=True``,
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and ``save_vecnormalize=True`` to also save replay buffer checkpoints
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and normalization statistics checkpoints.
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.. warning::
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When using multiple environments, each call to ``env.step()``
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will effectively correspond to ``n_envs`` steps.
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To account for that, you can use ``save_freq = max(save_freq // n_envs, 1)``
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:param save_freq: Save checkpoints every ``save_freq`` call of the callback.
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:param save_path: Path to the folder where the model will be saved.
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:param name_prefix: Common prefix to the saved models
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:param save_replay_buffer: Save the model replay buffer
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:param save_vecnormalize: Save the ``VecNormalize`` statistics
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:param verbose: Verbosity level: 0 for no output, 2 for indicating when saving model checkpoint
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"""
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def __init__(
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self,
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save_freq: int,
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save_path: str,
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name_prefix: str = "rl_model",
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save_replay_buffer: bool = False,
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save_vecnormalize: bool = False,
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verbose: int = 0,
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):
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super().__init__(verbose)
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self.save_freq = save_freq
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self.save_path = save_path
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self.name_prefix = name_prefix
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self.save_replay_buffer = save_replay_buffer
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self.save_vecnormalize = save_vecnormalize
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def _init_callback(self) -> None:
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# Create folder if needed
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if self.save_path is not None:
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os.makedirs(self.save_path, exist_ok=True)
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def _checkpoint_path(self, checkpoint_type: str = "", extension: str = "") -> str:
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"""
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Helper to get checkpoint path for each type of checkpoint.
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:param checkpoint_type: empty for the model, "replay_buffer_"
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or "vecnormalize_" for the other checkpoints.
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:param extension: Checkpoint file extension (zip for model, pkl for others)
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:return: Path to the checkpoint
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"""
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return os.path.join(self.save_path, f"{self.name_prefix}_{checkpoint_type}{self.num_timesteps}_steps.{extension}")
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def _on_step(self) -> bool:
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if self.n_calls % self.save_freq == 0:
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model_path = self._checkpoint_path(extension="zip")
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self.model.save(model_path)
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if self.verbose >= 2:
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print(f"Saving model checkpoint to {model_path}")
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if self.save_replay_buffer and hasattr(self.model, "replay_buffer") and self.model.replay_buffer is not None:
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# If model has a replay buffer, save it too
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replay_buffer_path = self._checkpoint_path("replay_buffer_", extension="pkl")
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self.model.save_replay_buffer(replay_buffer_path)
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if self.verbose > 1:
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print(f"Saving model replay buffer checkpoint to {replay_buffer_path}")
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if self.save_vecnormalize and self.model.get_vec_normalize_env() is not None:
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# Save the VecNormalize statistics
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vec_normalize_path = self._checkpoint_path("vecnormalize_", extension="pkl")
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self.model.get_vec_normalize_env().save(vec_normalize_path)
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if self.verbose >= 2:
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print(f"Saving model VecNormalize to {vec_normalize_path}")
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return True
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class ConvertCallback(BaseCallback):
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"""
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Convert functional callback (old-style) to object.
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:param callback:
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:param verbose: Verbosity level: 0 for no output, 1 for info messages, 2 for debug messages
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"""
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def __init__(self, callback: Callable[[Dict[str, Any], Dict[str, Any]], bool], verbose: int = 0):
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super().__init__(verbose)
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self.callback = callback
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def _on_step(self) -> bool:
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if self.callback is not None:
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return self.callback(self.locals, self.globals)
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return True
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class EvalCallback(EventCallback):
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"""
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Callback for evaluating an agent.
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.. warning::
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When using multiple environments, each call to ``env.step()``
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will effectively correspond to ``n_envs`` steps.
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To account for that, you can use ``eval_freq = max(eval_freq // n_envs, 1)``
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:param eval_env: The environment used for initialization
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:param callback_on_new_best: Callback to trigger
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when there is a new best model according to the ``mean_reward``
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:param callback_after_eval: Callback to trigger after every evaluation
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:param n_eval_episodes: The number of episodes to test the agent
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:param eval_freq: Evaluate the agent every ``eval_freq`` call of the callback.
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:param log_path: Path to a folder where the evaluations (``evaluations.npz``)
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will be saved. It will be updated at each evaluation.
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:param best_model_save_path: Path to a folder where the best model
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according to performance on the eval env will be saved.
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:param deterministic: Whether the evaluation should
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use a stochastic or deterministic actions.
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:param render: Whether to render or not the environment during evaluation
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:param verbose: Verbosity level: 0 for no output, 1 for indicating information about evaluation results
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:param warn: Passed to ``evaluate_policy`` (warns if ``eval_env`` has not been
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wrapped with a Monitor wrapper)
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"""
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def __init__(
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self,
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eval_env: Union[gym.Env, VecEnv],
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callback_on_new_best: Optional[BaseCallback] = None,
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callback_after_eval: Optional[BaseCallback] = None,
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n_eval_episodes: int = 5,
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eval_freq: int = 10000,
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log_path: Optional[str] = None,
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best_model_save_path: Optional[str] = None,
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deterministic: bool = True,
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render: bool = False,
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verbose: int = 1,
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warn: bool = True,
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):
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super().__init__(callback_after_eval, verbose=verbose)
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self.callback_on_new_best = callback_on_new_best
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if self.callback_on_new_best is not None:
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# Give access to the parent
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self.callback_on_new_best.parent = self
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self.n_eval_episodes = n_eval_episodes
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self.eval_freq = eval_freq
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self.best_mean_reward = -np.inf
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self.last_mean_reward = -np.inf
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self.deterministic = deterministic
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self.render = render
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self.warn = warn
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# Convert to VecEnv for consistency
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if not isinstance(eval_env, VecEnv):
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eval_env = DummyVecEnv([lambda: eval_env])
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self.eval_env = eval_env
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self.best_model_save_path = best_model_save_path
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# Logs will be written in ``evaluations.npz``
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if log_path is not None:
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log_path = os.path.join(log_path, "evaluations")
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self.log_path = log_path
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self.evaluations_results = []
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self.evaluations_timesteps = []
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self.evaluations_length = []
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# For computing success rate
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self._is_success_buffer = []
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self.evaluations_successes = []
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def _init_callback(self) -> None:
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# Does not work in some corner cases, where the wrapper is not the same
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if not isinstance(self.training_env, type(self.eval_env)):
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warnings.warn("Training and eval env are not of the same type" f"{self.training_env} != {self.eval_env}")
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# Create folders if needed
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if self.best_model_save_path is not None:
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os.makedirs(self.best_model_save_path, exist_ok=True)
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if self.log_path is not None:
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os.makedirs(os.path.dirname(self.log_path), exist_ok=True)
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# Init callback called on new best model
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if self.callback_on_new_best is not None:
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self.callback_on_new_best.init_callback(self.model)
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def _log_success_callback(self, locals_: Dict[str, Any], globals_: Dict[str, Any]) -> None:
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"""
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Callback passed to the ``evaluate_policy`` function
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in order to log the success rate (when applicable),
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for instance when using HER.
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:param locals_:
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:param globals_:
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"""
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info = locals_["info"]
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if locals_["done"]:
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maybe_is_success = info.get("is_success")
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if maybe_is_success is not None:
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self._is_success_buffer.append(maybe_is_success)
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def _on_step(self) -> bool:
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continue_training = True
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if self.eval_freq > 0 and self.n_calls % self.eval_freq == 0:
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# Sync training and eval env if there is VecNormalize
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if self.model.get_vec_normalize_env() is not None:
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try:
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sync_envs_normalization(self.training_env, self.eval_env)
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except AttributeError as e:
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raise AssertionError(
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"Training and eval env are not wrapped the same way, "
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"see https://stable-baselines3.readthedocs.io/en/master/guide/callbacks.html#evalcallback "
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"and warning above."
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) from e
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# Reset success rate buffer
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self._is_success_buffer = []
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episode_rewards, episode_lengths = evaluate_policy(
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self.model,
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self.eval_env,
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n_eval_episodes=self.n_eval_episodes,
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render=self.render,
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deterministic=self.deterministic,
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return_episode_rewards=True,
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warn=self.warn,
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callback=self._log_success_callback,
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)
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if self.log_path is not None:
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self.evaluations_timesteps.append(self.num_timesteps)
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self.evaluations_results.append(episode_rewards)
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self.evaluations_length.append(episode_lengths)
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kwargs = {}
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# Save success log if present
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if len(self._is_success_buffer) > 0:
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self.evaluations_successes.append(self._is_success_buffer)
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kwargs = dict(successes=self.evaluations_successes)
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np.savez(
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self.log_path,
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timesteps=self.evaluations_timesteps,
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results=self.evaluations_results,
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ep_lengths=self.evaluations_length,
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**kwargs,
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)
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mean_reward, std_reward = np.mean(episode_rewards), np.std(episode_rewards)
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mean_ep_length, std_ep_length = np.mean(episode_lengths), np.std(episode_lengths)
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self.last_mean_reward = mean_reward
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if self.verbose >= 1:
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print(f"Eval num_timesteps={self.num_timesteps}, " f"episode_reward={mean_reward:.2f} +/- {std_reward:.2f}")
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print(f"Episode length: {mean_ep_length:.2f} +/- {std_ep_length:.2f}")
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# Add to current Logger
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self.logger.record("eval/mean_reward", float(mean_reward))
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self.logger.record("eval/mean_ep_length", mean_ep_length)
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if len(self._is_success_buffer) > 0:
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success_rate = np.mean(self._is_success_buffer)
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if self.verbose >= 1:
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print(f"Success rate: {100 * success_rate:.2f}%")
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self.logger.record("eval/success_rate", success_rate)
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# Dump log so the evaluation results are printed with the correct timestep
|
||
self.logger.record("time/total_timesteps", self.num_timesteps, exclude="tensorboard")
|
||
self.logger.dump(self.num_timesteps)
|
||
|
||
if mean_reward > self.best_mean_reward:
|
||
if self.verbose >= 1:
|
||
print("New best mean reward!")
|
||
if self.best_model_save_path is not None:
|
||
self.model.save(os.path.join(self.best_model_save_path, "best_model"))
|
||
self.best_mean_reward = mean_reward
|
||
# Trigger callback on new best model, if needed
|
||
if self.callback_on_new_best is not None:
|
||
continue_training = self.callback_on_new_best.on_step()
|
||
|
||
# Trigger callback after every evaluation, if needed
|
||
if self.callback is not None:
|
||
continue_training = continue_training and self._on_event()
|
||
|
||
return continue_training
|
||
|
||
def update_child_locals(self, locals_: Dict[str, Any]) -> None:
|
||
"""
|
||
Update the references to the local variables.
|
||
|
||
:param locals_: the local variables during rollout collection
|
||
"""
|
||
if self.callback:
|
||
self.callback.update_locals(locals_)
|
||
|
||
|
||
class StopTrainingOnRewardThreshold(BaseCallback):
|
||
"""
|
||
Stop the training once a threshold in episodic reward
|
||
has been reached (i.e. when the model is good enough).
|
||
|
||
It must be used with the ``EvalCallback``.
|
||
|
||
:param reward_threshold: Minimum expected reward per episode
|
||
to stop training.
|
||
:param verbose: Verbosity level: 0 for no output, 1 for indicating when training ended because episodic reward
|
||
threshold reached
|
||
"""
|
||
|
||
def __init__(self, reward_threshold: float, verbose: int = 0):
|
||
super().__init__(verbose=verbose)
|
||
self.reward_threshold = reward_threshold
|
||
|
||
def _on_step(self) -> bool:
|
||
assert self.parent is not None, "``StopTrainingOnMinimumReward`` callback must be used " "with an ``EvalCallback``"
|
||
# Convert np.bool_ to bool, otherwise callback() is False won't work
|
||
continue_training = bool(self.parent.best_mean_reward < self.reward_threshold)
|
||
if self.verbose >= 1 and not continue_training:
|
||
print(
|
||
f"Stopping training because the mean reward {self.parent.best_mean_reward:.2f} "
|
||
f" is above the threshold {self.reward_threshold}"
|
||
)
|
||
return continue_training
|
||
|
||
|
||
class EveryNTimesteps(EventCallback):
|
||
"""
|
||
Trigger a callback every ``n_steps`` timesteps
|
||
|
||
:param n_steps: Number of timesteps between two trigger.
|
||
:param callback: Callback that will be called
|
||
when the event is triggered.
|
||
"""
|
||
|
||
def __init__(self, n_steps: int, callback: BaseCallback):
|
||
super().__init__(callback)
|
||
self.n_steps = n_steps
|
||
self.last_time_trigger = 0
|
||
|
||
def _on_step(self) -> bool:
|
||
if (self.num_timesteps - self.last_time_trigger) >= self.n_steps:
|
||
self.last_time_trigger = self.num_timesteps
|
||
return self._on_event()
|
||
return True
|
||
|
||
|
||
class StopTrainingOnMaxEpisodes(BaseCallback):
|
||
"""
|
||
Stop the training once a maximum number of episodes are played.
|
||
|
||
For multiple environments presumes that, the desired behavior is that the agent trains on each env for ``max_episodes``
|
||
and in total for ``max_episodes * n_envs`` episodes.
|
||
|
||
:param max_episodes: Maximum number of episodes to stop training.
|
||
:param verbose: Verbosity level: 0 for no output, 1 for indicating information about when training ended by
|
||
reaching ``max_episodes``
|
||
"""
|
||
|
||
def __init__(self, max_episodes: int, verbose: int = 0):
|
||
super().__init__(verbose=verbose)
|
||
self.max_episodes = max_episodes
|
||
self._total_max_episodes = max_episodes
|
||
self.n_episodes = 0
|
||
|
||
def _init_callback(self) -> None:
|
||
# At start set total max according to number of envirnments
|
||
self._total_max_episodes = self.max_episodes * self.training_env.num_envs
|
||
|
||
def _on_step(self) -> bool:
|
||
# Check that the `dones` local variable is defined
|
||
assert "dones" in self.locals, "`dones` variable is not defined, please check your code next to `callback.on_step()`"
|
||
self.n_episodes += np.sum(self.locals["dones"]).item()
|
||
|
||
continue_training = self.n_episodes < self._total_max_episodes
|
||
|
||
if self.verbose >= 1 and not continue_training:
|
||
mean_episodes_per_env = self.n_episodes / self.training_env.num_envs
|
||
mean_ep_str = (
|
||
f"with an average of {mean_episodes_per_env:.2f} episodes per env" if self.training_env.num_envs > 1 else ""
|
||
)
|
||
|
||
print(
|
||
f"Stopping training with a total of {self.num_timesteps} steps because the "
|
||
f"{self.locals.get('tb_log_name')} model reached max_episodes={self.max_episodes}, "
|
||
f"by playing for {self.n_episodes} episodes "
|
||
f"{mean_ep_str}"
|
||
)
|
||
return continue_training
|
||
|
||
|
||
class StopTrainingOnNoModelImprovement(BaseCallback):
|
||
"""
|
||
Stop the training early if there is no new best model (new best mean reward) after more than N consecutive evaluations.
|
||
|
||
It is possible to define a minimum number of evaluations before start to count evaluations without improvement.
|
||
|
||
It must be used with the ``EvalCallback``.
|
||
|
||
:param max_no_improvement_evals: Maximum number of consecutive evaluations without a new best model.
|
||
:param min_evals: Number of evaluations before start to count evaluations without improvements.
|
||
:param verbose: Verbosity level: 0 for no output, 1 for indicating when training ended because no new best model
|
||
"""
|
||
|
||
def __init__(self, max_no_improvement_evals: int, min_evals: int = 0, verbose: int = 0):
|
||
super().__init__(verbose=verbose)
|
||
self.max_no_improvement_evals = max_no_improvement_evals
|
||
self.min_evals = min_evals
|
||
self.last_best_mean_reward = -np.inf
|
||
self.no_improvement_evals = 0
|
||
|
||
def _on_step(self) -> bool:
|
||
assert self.parent is not None, "``StopTrainingOnNoModelImprovement`` callback must be used with an ``EvalCallback``"
|
||
|
||
continue_training = True
|
||
|
||
if self.n_calls > self.min_evals:
|
||
if self.parent.best_mean_reward > self.last_best_mean_reward:
|
||
self.no_improvement_evals = 0
|
||
else:
|
||
self.no_improvement_evals += 1
|
||
if self.no_improvement_evals > self.max_no_improvement_evals:
|
||
continue_training = False
|
||
|
||
self.last_best_mean_reward = self.parent.best_mean_reward
|
||
|
||
if self.verbose >= 1 and not continue_training:
|
||
print(
|
||
f"Stopping training because there was no new best model in the last {self.no_improvement_evals:d} evaluations"
|
||
)
|
||
|
||
return continue_training
|
||
|
||
|
||
class ProgressBarCallback(BaseCallback):
|
||
"""
|
||
Display a progress bar when training SB3 agent
|
||
using tqdm and rich packages.
|
||
"""
|
||
|
||
def __init__(self) -> None:
|
||
super().__init__()
|
||
if tqdm is None:
|
||
raise ImportError(
|
||
"You must install tqdm and rich in order to use the progress bar callback. "
|
||
"It is included if you install stable-baselines with the extra packages: "
|
||
"`pip install stable-baselines3[extra]`"
|
||
)
|
||
self.pbar = None
|
||
|
||
def _on_training_start(self) -> None:
|
||
# Initialize progress bar
|
||
# Remove timesteps that were done in previous training sessions
|
||
self.pbar = tqdm(total=self.locals["total_timesteps"] - self.model.num_timesteps)
|
||
|
||
def _on_step(self) -> bool:
|
||
# Update progress bar, we do num_envs steps per call to `env.step()`
|
||
self.pbar.update(self.training_env.num_envs)
|
||
return True
|
||
|
||
def _on_training_end(self) -> None:
|
||
# Flush and close progress bar
|
||
self.pbar.refresh()
|
||
self.pbar.close()
|