mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-27 22:55:17 +00:00
1f0dd60b97
* removed policy from save, changed th.loads to map to device * found hack: catch pickle exception and trying th.load with mapping instead, otherwise raise exception with more information -> loading cuda on cpu raises exception -> leads to th.load with map being called * deleted todo * updated changelog * start of saving refactor * first working c * all tests pass, save refactored * - backwards compatibilty not always - make pytest all passing - make typing all passing * Fixes and simplify the save method * Remove unused param * Fix backward compat * Fix docstring
793 lines
34 KiB
Python
793 lines
34 KiB
Python
import time
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import os
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import io
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import zipfile
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import typing
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from typing import Union, Type, Optional, Dict, Any, List, Tuple
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from abc import ABC, abstractmethod
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from collections import deque
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import gym
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import torch as th
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import numpy as np
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from torchy_baselines.common import logger
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from torchy_baselines.common.policies import BasePolicy, get_policy_from_name
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from torchy_baselines.common.utils import set_random_seed, get_schedule_fn, update_learning_rate
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from torchy_baselines.common.vec_env import DummyVecEnv, VecEnv, unwrap_vec_normalize, sync_envs_normalization
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from torchy_baselines.common.monitor import Monitor
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from torchy_baselines.common.evaluation import evaluate_policy
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from torchy_baselines.common.save_util import data_to_json, json_to_data, recursive_getattr, recursive_setattr
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# TODO: define aliases, ex GymEnv = Union[gym.Env, VecEnv]
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if typing.TYPE_CHECKING:
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from torchy_baselines.common.noise import ActionNoise
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class BaseRLModel(ABC):
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"""
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The base RL model
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:param policy: Policy object
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:param env: The environment to learn from
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(if registered in Gym, can be str. Can be None for loading trained models)
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:param policy_base: The base policy used by this method
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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 none, 1 training information, 2 debug
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:param device: Device on which the code should run.
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By default, it will try to use a Cuda compatible device and fallback to cpu
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if it is not possible.
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:param support_multi_env: Whether the algorithm supports training
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with multiple environments (as in A2C)
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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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:param monitor_wrapper: When creating an environment, whether to wrap it
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or not in a Monitor wrapper.
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:param seed: Seed for the pseudo random generators
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:param use_sde: Whether to use State Dependent Exploration (SDE)
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instead of action noise exploration (default: False)
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:param sde_sample_freq: Sample a new noise matrix every n steps when using SDE
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Default: -1 (only sample at the beginning of the rollout)
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"""
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def __init__(self,
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policy: Type[BasePolicy],
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env: Union[gym.Env, VecEnv, str],
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policy_base: Type[BasePolicy],
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policy_kwargs: Dict[str, Any] = None,
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verbose: int = 0,
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device: Union[th.device, str] = 'auto',
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support_multi_env: bool = False,
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create_eval_env: bool = False,
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monitor_wrapper: bool = True,
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seed: Optional[int] = None,
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use_sde: bool = False,
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sde_sample_freq: int = -1):
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if isinstance(policy, str) and policy_base is not None:
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self.policy_class = get_policy_from_name(policy_base, policy)
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else:
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self.policy_class = policy
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if device == 'auto':
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device = 'cuda' if th.cuda.is_available() else 'cpu'
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self.device = th.device(device)
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if verbose > 0:
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print(f"Using {self.device} device")
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self.env = None # type: Union[gym.Env, VecEnv]
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# get VecNormalize object if needed
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self._vec_normalize_env = unwrap_vec_normalize(env)
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self.verbose = verbose
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self.policy_kwargs = {} if policy_kwargs is None else policy_kwargs
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self.observation_space = None
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self.action_space = None
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self.n_envs = None
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self.num_timesteps = 0
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self.eval_env = None
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self.replay_buffer = None
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self.seed = seed
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self.action_noise = None # type: ActionNoise
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self.start_time = None
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self.policy, self.actor = None, None
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self.learning_rate = None
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# Used for SDE only
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self.rollout_data = None
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self.on_policy_exploration = False
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self.use_sde = use_sde
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self.sde_sample_freq = sde_sample_freq
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# Track the training progress (from 1 to 0)
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# this is used to update the learning rate
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self._current_progress = 1
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# Create and wrap the env if needed
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if env is not None:
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if isinstance(env, str):
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if create_eval_env:
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eval_env = gym.make(env)
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if monitor_wrapper:
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eval_env = Monitor(eval_env, filename=None)
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self.eval_env = DummyVecEnv([lambda: eval_env])
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if self.verbose >= 1:
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print("Creating environment from the given name, wrapped in a DummyVecEnv.")
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env = gym.make(env)
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if monitor_wrapper:
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env = Monitor(env, filename=None)
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env = DummyVecEnv([lambda: env])
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self.observation_space = env.observation_space
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self.action_space = env.action_space
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if not isinstance(env, VecEnv):
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if self.verbose >= 1:
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print("Wrapping the env in a DummyVecEnv.")
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env = DummyVecEnv([lambda: env])
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self.n_envs = env.num_envs
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self.env = env
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if not support_multi_env and self.n_envs > 1:
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raise ValueError("Error: the model does not support multiple envs requires a single vectorized"
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" environment.")
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@abstractmethod
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def _setup_model(self) -> None:
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"""
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Setup model so state_dict can be loaded
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"""
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raise NotImplementedError()
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def _get_eval_env(self, eval_env: Union[gym.Env, VecEnv, None]) -> Union[gym.Env, VecEnv, None]:
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"""
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Return the environment that will be used for evaluation.
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:param eval_env:
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:return:
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"""
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if eval_env is None:
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eval_env = self.eval_env
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if eval_env is not None:
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if not isinstance(eval_env, VecEnv):
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eval_env = DummyVecEnv([lambda: eval_env])
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assert eval_env.num_envs == 1
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return eval_env
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def scale_action(self, action: np.ndarray) -> np.ndarray:
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"""
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Rescale the action from [low, high] to [-1, 1]
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(no need for symmetric action space)
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:param action:
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:return:
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"""
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low, high = self.action_space.low, self.action_space.high
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return 2.0 * ((action - low) / (high - low)) - 1.0
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def unscale_action(self, scaled_action: np.ndarray) -> np.ndarray:
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"""
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Rescale the action from [-1, 1] to [low, high]
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(no need for symmetric action space)
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:param scaled_action:
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:return:
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"""
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low, high = self.action_space.low, self.action_space.high
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return low + (0.5 * (scaled_action + 1.0) * (high - low))
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def _setup_learning_rate(self) -> None:
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"""Transform to callable if needed."""
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self.learning_rate = get_schedule_fn(self.learning_rate)
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def _update_current_progress(self, num_timesteps: int, total_timesteps: int) -> None:
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"""
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Compute current progress (from 1 to 0)
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:param num_timesteps: current number of timesteps
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:param total_timesteps:
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"""
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self._current_progress = 1.0 - float(num_timesteps) / float(total_timesteps)
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def _update_learning_rate(self, optimizers: Union[List[th.optim.Optimizer], th.optim.Optimizer]) -> None:
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"""
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Update the optimizers learning rate using the current learning rate schedule
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and the current progress (from 1 to 0).
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:param optimizers: An optimizer or a list of optimizer.
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"""
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# Log the current learning rate
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logger.logkv("learning_rate", self.learning_rate(self._current_progress))
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if not isinstance(optimizers, list):
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optimizers = [optimizers]
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for optimizer in optimizers:
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update_learning_rate(optimizer, self.learning_rate(self._current_progress))
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@staticmethod
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def safe_mean(arr: Union[np.ndarray, list]) -> np.ndarray:
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"""
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Compute the mean of an array if there is at least one element.
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For empty array, return NaN. It is used for logging only.
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:param arr:
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:return:
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"""
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return np.nan if len(arr) == 0 else np.mean(arr)
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def get_env(self) -> Union[VecEnv, None]:
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"""
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Returns the current environment (can be None if not defined).
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:return: The current environment
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"""
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return self.env
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@staticmethod
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def check_env(env, observation_space: gym.spaces.Space, action_space: gym.spaces.Space) -> bool:
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"""
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Checks the validity of the environment and returns if it is consistent.
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Checked parameters:
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- observation_space
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- action_space
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:return: True if environment seems to be coherent
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"""
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if observation_space != env.observation_space:
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return False
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if action_space != env.action_space:
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return False
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# return true if no check failed
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return True
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def set_env(self, env: Union[gym.Env, VecEnv]) -> None:
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"""
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Checks the validity of the environment, and if it is coherent, set it as the current environment.
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Furthermore wrap any non vectorized env into a vectorized
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checked parameters:
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- observation_space
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- action_space
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:param env: The environment for learning a policy
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"""
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if self.check_env(env, self.observation_space, self.action_space) is False:
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raise ValueError("The given environment is not compatible with model: "
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"observation and action spaces do not match")
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# it must be coherent now
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# if it is not a VecEnv, make it a VecEnv
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if not isinstance(env, VecEnv):
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if self.verbose >= 1:
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print("Wrapping the env in a DummyVecEnv.")
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env = DummyVecEnv([lambda: env])
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self.n_envs = env.num_envs
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self.env = env
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def get_torch_variables(self) -> Tuple[List[str], List[str]]:
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"""
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Get the name of the torch variable that will be saved.
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`th.save` and `th.load` will be used with the right device
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instead of the default pickling strategy.
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:return: (Tuple[List[str], List[str]])
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name of the variables with state dicts to save, name of additional torch tensors,
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"""
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state_dicts = ["policy"]
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return state_dicts, []
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@abstractmethod
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def learn(self, total_timesteps: int,
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callback=None, log_interval: int = 100,
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tb_log_name: str = "run",
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eval_env: Union[gym.Env, VecEnv, None] = None,
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eval_freq: int = -1,
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n_eval_episodes: int = 5,
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reset_num_timesteps: bool = True):
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"""
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Return a trained model.
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:param total_timesteps: (int) The total number of samples to train on
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:param callback: (function (dict, dict)) -> boolean function called at every steps with state of the algorithm.
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It takes the local and global variables. If it returns False, training is aborted.
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:param log_interval: (int) The number of timesteps before logging.
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:param tb_log_name: (str) the name of the run for tensorboard log
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:param reset_num_timesteps: (bool) whether or not to reset the current timestep number (used in logging)
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:param eval_env: (gym.Env) Environment that will be used to evaluate the agent
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:param eval_freq: (int) Evaluate the agent every `eval_freq` timesteps (this may vary a little)
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:param n_eval_episodes: (int) Number of episode to evaluate the agent
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:return: (BaseRLModel) the trained model
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"""
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raise NotImplementedError()
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@abstractmethod
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def predict(self, observation: np.ndarray,
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state: Optional[np.ndarray] = None,
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mask: Optional[np.ndarray] = None,
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deterministic: bool = False) -> np.ndarray:
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"""
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Get the model's action from an observation
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:param observation: the input observation
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:param state: The last states (can be None, used in recurrent policies)
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:param mask: The last masks (can be None, used in recurrent policies)
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:param deterministic: Whether or not to return deterministic actions.
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:return: the model's action and the next state (used in recurrent policies)
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"""
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raise NotImplementedError()
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@classmethod
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def load(cls, load_path: str, env: Union[gym.Env, VecEnv, None] = None, **kwargs):
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"""
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Load the model from a zip-file
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:param load_path: the location of the saved data
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:param env: the new environment to run the loaded model on
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(can be None if you only need prediction from a trained model) has priority over any saved environment
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:param kwargs: extra arguments to change the model when loading
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"""
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data, params, tensors = cls._load_from_file(load_path)
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if 'policy_kwargs' in kwargs and kwargs['policy_kwargs'] != data['policy_kwargs']:
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raise ValueError(f"The specified policy kwargs do not equal the stored policy kwargs."
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"Stored kwargs: {data['policy_kwargs']}, specified kwargs: {kwargs['policy_kwargs']}")
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# check if observation space and action space are part of the saved parameters
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if ("observation_space" not in data or "action_space" not in data) and "env" not in data:
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raise ValueError("The observation_space and action_space was not given, can't verify new environments")
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# check if given env is valid
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if env is not None and cls.check_env(env, data["observation_space"], data["action_space"]) is False:
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raise ValueError("The given environment does not comply to the model")
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# if no new env was given use stored env if possible
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if env is None and "env" in data:
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env = data["env"]
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# first create model, but only setup if a env was given
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# noinspection PyArgumentList
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model = cls(policy=data["policy_class"], env=env, device='auto', _init_setup_model=env is not None)
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# load parameters
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model.__dict__.update(data)
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model.__dict__.update(kwargs)
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if not hasattr(model, "_setup_model") and len(params) > 0:
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raise NotImplementedError("loading was executed on a model that has no means to create the policies")
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model._setup_model()
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# put state_dicts back in place
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for name in params:
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attr = recursive_getattr(model, name)
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attr.load_state_dict(params[name])
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# put tensors back in place
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if tensors is not None:
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for name in tensors:
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recursive_setattr(model, name, tensors[name])
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return model
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@staticmethod
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def _load_from_file(load_path: str, load_data: bool = True):
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""" Load model data from a .zip archive
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:param load_path: Where to load the model from
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:param load_data: Whether we should load and return data
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(class parameters). Mainly used by 'load_parameters' to only load model parameters (weights)
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:return: (dict),(dict),(dict) Class parameters, model state_dicts (dict of state_dict)
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and dict of extra tensors
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"""
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# Check if file exists if load_path is a string
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if isinstance(load_path, str):
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if not os.path.exists(load_path):
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if os.path.exists(load_path + ".zip"):
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load_path += ".zip"
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else:
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raise ValueError(f"Error: the file {load_path} could not be found")
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# set device to cpu if cuda is not available
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if th.cuda.is_available():
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device = th.device('cuda')
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else:
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device = th.device('cpu')
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# Open the zip archive and load data
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try:
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with zipfile.ZipFile(load_path, "r") as archive:
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namelist = archive.namelist()
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# If data or parameters is not in the
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# zip archive, assume they were stored
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# as None (_save_to_file_zip allows this).
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data = None
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tensors = None
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params = {}
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if "data" in namelist and load_data:
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# Load class parameters and convert to string
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json_data = archive.read("data").decode()
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data = json_to_data(json_data, device)
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if "tensors.pth" in namelist and load_data:
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# Load extra tensors
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with archive.open('tensors.pth', mode="r") as tensor_file:
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# File has to be seekable, but opt_param_file is not, so load in BytesIO first
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# fixed in python >= 3.7
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file_content = io.BytesIO()
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file_content.write(tensor_file.read())
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# go to start of file
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file_content.seek(0)
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# load the parameters with the right `map_location`
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tensors = th.load(file_content, map_location=device)
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# check for all other .pth files
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other_files = [file_name for file_name in namelist if
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os.path.splitext(file_name)[1] == ".pth" and file_name != "tensors.pth"]
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# if there are any other files which end with .pth and aren't "params.pth"
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# assume that they each are optimizer parameters
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if len(other_files) > 0:
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for file_path in other_files:
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with archive.open(file_path, mode="r") as opt_param_file:
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# File has to be seekable, but opt_param_file is not, so load in BytesIO first
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# fixed in python >= 3.7
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file_content = io.BytesIO()
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file_content.write(opt_param_file.read())
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# go to start of file
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file_content.seek(0)
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# load the parameters with the right `map_location`
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params[os.path.splitext(file_path)[0]] = th.load(file_content, map_location=device)
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# for backward compatibility
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if params.get('params') is not None:
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params_copy = {}
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for name in params:
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if name == 'params':
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params_copy['policy'] = params[name]
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elif name == 'opt':
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params_copy['policy.optimizer'] = params[name]
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# Special case for SAC
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elif name == 'ent_coef_optimizer':
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params_copy[name] = params[name]
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else:
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params_copy[name + '.optimizer'] = params[name]
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params = params_copy
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except zipfile.BadZipFile:
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# load_path wasn't a zip file
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raise ValueError(f"Error: the file {load_path} wasn't a zip-file")
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return data, params, tensors
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def set_random_seed(self, seed: Optional[int] = None) -> None:
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"""
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Set the seed of the pseudo-random generators
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(python, numpy, pytorch, gym, action_space)
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:param seed: (int)
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"""
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if seed is None:
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return
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set_random_seed(seed, using_cuda=self.device == th.device('cuda'))
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self.action_space.seed(seed)
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if self.env is not None:
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self.env.seed(seed)
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|
if self.eval_env is not None:
|
|
self.eval_env.seed(seed)
|
|
|
|
def _setup_learn(self, eval_env):
|
|
"""
|
|
Initialize different variables needed for training.
|
|
|
|
:param eval_env: (gym.Env or VecEnv)
|
|
:return: (int, int, [float], np.ndarray, VecEnv)
|
|
"""
|
|
self.start_time = time.time()
|
|
self.ep_info_buffer = deque(maxlen=100)
|
|
|
|
if self.action_noise is not None:
|
|
self.action_noise.reset()
|
|
|
|
timesteps_since_eval, episode_num = 0, 0
|
|
evaluations = []
|
|
|
|
if eval_env is not None and self.seed is not None:
|
|
eval_env.seed(self.seed)
|
|
|
|
eval_env = self._get_eval_env(eval_env)
|
|
obs = self.env.reset() # type: Union[gym.Env, VecEnv]
|
|
return timesteps_since_eval, episode_num, evaluations, obs, eval_env
|
|
|
|
def _update_info_buffer(self, infos):
|
|
"""
|
|
Retrieve reward and episode length and update the buffer
|
|
if using Monitor wrapper.
|
|
|
|
:param infos: ([dict])
|
|
"""
|
|
for info in infos:
|
|
maybe_ep_info = info.get('episode')
|
|
if maybe_ep_info is not None:
|
|
self.ep_info_buffer.extend([maybe_ep_info])
|
|
|
|
def collect_rollouts(self, env, n_episodes=1, n_steps=-1, action_noise=None,
|
|
deterministic=False, callback=None,
|
|
learning_starts=0, num_timesteps=0,
|
|
replay_buffer=None, obs=None,
|
|
episode_num=0, log_interval=None):
|
|
"""
|
|
Collect rollout using the current policy (and possibly fill the replay buffer)
|
|
TODO: move this method to off-policy base class.
|
|
|
|
:param env: (VecEnv)
|
|
:param n_episodes: (int)
|
|
:param n_steps: (int)
|
|
:param action_noise: (ActionNoise)
|
|
:param deterministic: (bool)
|
|
:param callback: (callable)
|
|
:param learning_starts: (int)
|
|
:param num_timesteps: (int)
|
|
:param replay_buffer: (ReplayBuffer)
|
|
:param obs: (np.ndarray)
|
|
:param episode_num: (int)
|
|
:param log_interval: (int)
|
|
"""
|
|
episode_rewards = []
|
|
total_timesteps = []
|
|
total_steps, total_episodes = 0, 0
|
|
assert isinstance(env, VecEnv)
|
|
assert env.num_envs == 1
|
|
|
|
# Retrieve unnormalized observation for saving into the buffer
|
|
if self._vec_normalize_env is not None:
|
|
obs_ = self._vec_normalize_env.get_original_obs()
|
|
|
|
self.rollout_data = None
|
|
if self.use_sde:
|
|
self.actor.reset_noise()
|
|
# Reset rollout data
|
|
if self.on_policy_exploration:
|
|
self.rollout_data = {key: [] for key in ['observations', 'actions', 'rewards', 'dones', 'values']}
|
|
|
|
while total_steps < n_steps or total_episodes < n_episodes:
|
|
done = False
|
|
# Reset environment: not needed for VecEnv
|
|
# obs = env.reset()
|
|
episode_reward, episode_timesteps = 0.0, 0
|
|
|
|
while not done:
|
|
if self.use_sde and self.sde_sample_freq > 0 and n_steps % self.sde_sample_freq == 0:
|
|
# Sample a new noise matrix
|
|
self.actor.reset_noise()
|
|
|
|
# Select action randomly or according to policy
|
|
# TODO: use action from policy when using SDE during the warmup phase?
|
|
# if num_timesteps < learning_starts and not self.use_sde:
|
|
if num_timesteps < learning_starts:
|
|
# Warmup phase
|
|
unscaled_action = np.array([self.action_space.sample()])
|
|
else:
|
|
unscaled_action = self.predict(obs, deterministic=not self.use_sde)
|
|
|
|
# Rescale the action from [low, high] to [-1, 1]
|
|
scaled_action = self.scale_action(unscaled_action)
|
|
|
|
if self.use_sde:
|
|
# When using SDE, the action can be out of bounds
|
|
# TODO: fix with squashing and account for that in the proba distribution
|
|
clipped_action = np.clip(scaled_action, -1, 1)
|
|
else:
|
|
clipped_action = scaled_action
|
|
|
|
# Add noise to the action (improve exploration)
|
|
if action_noise is not None:
|
|
# NOTE: in the original implementation of TD3, the noise was applied to the unscaled action
|
|
# Update(October 2019): Not anymore
|
|
clipped_action = np.clip(clipped_action + action_noise(), -1, 1)
|
|
|
|
# Rescale and perform action
|
|
new_obs, reward, done, infos = env.step(self.unscale_action(clipped_action))
|
|
|
|
done_bool = [float(done[0])]
|
|
episode_reward += reward
|
|
|
|
# Retrieve reward and episode length if using Monitor wrapper
|
|
self._update_info_buffer(infos)
|
|
|
|
# Store data in replay buffer
|
|
if replay_buffer is not None:
|
|
# Store only the unnormalized version
|
|
if self._vec_normalize_env is not None:
|
|
new_obs_ = self._vec_normalize_env.get_original_obs()
|
|
reward_ = self._vec_normalize_env.get_original_reward()
|
|
else:
|
|
# Avoid changing the original ones
|
|
obs_, new_obs_, reward_ = obs, new_obs, reward
|
|
|
|
replay_buffer.add(obs_, new_obs_, clipped_action, reward_, done_bool)
|
|
|
|
if self.rollout_data is not None:
|
|
# Assume only one env
|
|
self.rollout_data['observations'].append(obs[0].copy())
|
|
self.rollout_data['actions'].append(scaled_action[0].copy())
|
|
self.rollout_data['rewards'].append(reward[0].copy())
|
|
self.rollout_data['dones'].append(np.array(done_bool[0]).copy())
|
|
obs_tensor = th.FloatTensor(obs).to(self.device)
|
|
self.rollout_data['values'].append(self.vf_net(obs_tensor)[0].cpu().detach().numpy())
|
|
|
|
obs = new_obs
|
|
# Save the true unnormalized observation
|
|
# otherwise obs_ = self._vec_normalize_env.unnormalize_obs(obs)
|
|
# is a good approximation
|
|
if self._vec_normalize_env is not None:
|
|
obs_ = new_obs_
|
|
|
|
num_timesteps += 1
|
|
episode_timesteps += 1
|
|
total_steps += 1
|
|
if 0 < n_steps <= total_steps:
|
|
break
|
|
|
|
if done:
|
|
total_episodes += 1
|
|
episode_rewards.append(episode_reward)
|
|
total_timesteps.append(episode_timesteps)
|
|
# TODO: reset SDE matrix at the end of the episode?
|
|
if action_noise is not None:
|
|
action_noise.reset()
|
|
|
|
# Display training infos
|
|
if self.verbose >= 1 and log_interval is not None and (
|
|
episode_num + total_episodes) % log_interval == 0:
|
|
fps = int(num_timesteps / (time.time() - self.start_time))
|
|
logger.logkv("episodes", episode_num + total_episodes)
|
|
if len(self.ep_info_buffer) > 0 and len(self.ep_info_buffer[0]) > 0:
|
|
logger.logkv('ep_rew_mean', self.safe_mean([ep_info['r'] for ep_info in self.ep_info_buffer]))
|
|
logger.logkv('ep_len_mean', self.safe_mean([ep_info['l'] for ep_info in self.ep_info_buffer]))
|
|
# logger.logkv("n_updates", n_updates)
|
|
logger.logkv("fps", fps)
|
|
logger.logkv('time_elapsed', int(time.time() - self.start_time))
|
|
logger.logkv("total timesteps", num_timesteps)
|
|
if self.use_sde:
|
|
logger.logkv("std", (self.actor.get_std()).mean().item())
|
|
logger.dumpkvs()
|
|
|
|
mean_reward = np.mean(episode_rewards) if total_episodes > 0 else 0.0
|
|
|
|
# Post processing
|
|
if self.rollout_data is not None:
|
|
for key in ['observations', 'actions', 'rewards', 'dones', 'values']:
|
|
self.rollout_data[key] = th.FloatTensor(np.array(self.rollout_data[key])).to(self.device)
|
|
|
|
self.rollout_data['returns'] = self.rollout_data['rewards'].clone()
|
|
self.rollout_data['advantage'] = self.rollout_data['rewards'].clone()
|
|
|
|
# Compute return and advantage
|
|
last_return = 0.0
|
|
for step in reversed(range(len(self.rollout_data['rewards']))):
|
|
if step == len(self.rollout_data['rewards']) - 1:
|
|
next_non_terminal = 1.0 - done[0]
|
|
next_value = self.vf_net(th.FloatTensor(obs).to(self.device))[0].detach()
|
|
last_return = self.rollout_data['rewards'][step] + next_non_terminal * next_value
|
|
else:
|
|
next_non_terminal = 1.0 - self.rollout_data['dones'][step + 1]
|
|
last_return = self.rollout_data['rewards'][step] + self.gamma * last_return * next_non_terminal
|
|
self.rollout_data['returns'][step] = last_return
|
|
self.rollout_data['advantage'] = self.rollout_data['returns'] - self.rollout_data['values']
|
|
|
|
return mean_reward, total_steps, total_episodes, obs
|
|
|
|
@staticmethod
|
|
def _save_to_file_zip(save_path: str, data: Dict[str, Any] = None,
|
|
params: Dict[str, Any] = None, tensors: Dict[str, Any] = None) -> None:
|
|
"""
|
|
Save model to a zip archive.
|
|
|
|
:param save_path: Where to store the model
|
|
:param data: Class parameters being stored
|
|
:param params: Model parameters being stored expected to contain an entry for every
|
|
state_dict with its name and the state_dict
|
|
:param tensors: Extra tensor variables expected to contain name and value of tensors
|
|
"""
|
|
|
|
# data/params can be None, so do not
|
|
# try to serialize them blindly
|
|
if data is not None:
|
|
serialized_data = data_to_json(data)
|
|
|
|
# Check postfix if save_path is a string
|
|
if isinstance(save_path, str):
|
|
_, ext = os.path.splitext(save_path)
|
|
if ext == "":
|
|
save_path += ".zip"
|
|
|
|
# Create a zip-archive and write our objects
|
|
# there. This works when save_path is either
|
|
# str or a file-like
|
|
with zipfile.ZipFile(save_path, "w") as archive:
|
|
# Do not try to save "None" elements
|
|
if data is not None:
|
|
archive.writestr("data", serialized_data)
|
|
if tensors is not None:
|
|
with archive.open('tensors.pth', mode="w") as tensors_file:
|
|
th.save(tensors, tensors_file)
|
|
if params is not None:
|
|
for file_name, dict_ in params.items():
|
|
with archive.open(file_name + '.pth', mode="w") as param_file:
|
|
th.save(dict_, param_file)
|
|
|
|
def excluded_save_params(self) -> List[str]:
|
|
"""
|
|
Returns the names of the parameters that should be excluded by default
|
|
when saving the model.
|
|
|
|
:return: ([str]) List of parameters that should be excluded from save
|
|
"""
|
|
return ["policy", "device", "env", "eval_env", "replay_buffer", "rollout_buffer", "_vec_normalize_env"]
|
|
|
|
def save(self, path: str, exclude: Optional[List[str]] = None, include: Optional[List[str]] = None) -> None:
|
|
"""
|
|
Save all the attributes of the object and the model parameters in a zip-file.
|
|
|
|
:param path: path to the file where the rl agent should be saved
|
|
:param exclude: name of parameters that should be excluded in addition to the default one
|
|
:param include: name of parameters that might be excluded but should be included anyway
|
|
"""
|
|
# copy parameter list so we don't mutate the original dict
|
|
data = self.__dict__.copy()
|
|
# use standard list of excluded parameters if none given
|
|
if exclude is None:
|
|
exclude = self.excluded_save_params()
|
|
else:
|
|
# append standard exclude params to the given params
|
|
exclude.extend([param for param in self.excluded_save_params() if param not in exclude])
|
|
|
|
# do not exclude params if they are specifically included
|
|
if include is not None:
|
|
exclude = [param_name for param_name in exclude if param_name not in include]
|
|
|
|
state_dicts_names, tensors_names = self.get_torch_variables()
|
|
# any params that are in the save vars must not be saved by data
|
|
torch_variables = state_dicts_names + tensors_names
|
|
for torch_var in torch_variables:
|
|
# we need to get only the name of the top most module as we'll remove that
|
|
var_name = torch_var.split('.')[0]
|
|
exclude.append(var_name)
|
|
|
|
# Remove parameter entries of parameters which are to be excluded
|
|
for param_name in exclude:
|
|
if param_name in data:
|
|
data.pop(param_name, None)
|
|
|
|
# Build dict of tensor variables
|
|
tensors = None
|
|
if tensors_names is not None:
|
|
tensors = {}
|
|
for name in tensors_names:
|
|
attr = recursive_getattr(self, name)
|
|
tensors[name] = attr
|
|
|
|
# Build dict of state_dicts
|
|
params_to_save = {}
|
|
for name in state_dicts_names:
|
|
attr = recursive_getattr(self, name)
|
|
# Retrieve state dict
|
|
params_to_save[name] = attr.state_dict()
|
|
|
|
self._save_to_file_zip(path, data=data, params=params_to_save, tensors=tensors)
|
|
|
|
def _eval_policy(self, eval_freq: int, eval_env: int, n_eval_episodes: int,
|
|
timesteps_since_eval: int, render: bool = False, deterministic: bool = True) -> int:
|
|
"""
|
|
Evaluate the current policy on a test environment.
|
|
|
|
:param eval_freq: Evaluate the agent every `eval_freq` timesteps (this may vary a little)
|
|
:param n_eval_episodes: Number of episode to evaluate the agent
|
|
:parma timesteps_since_eval: Number of timesteps since last evaluation
|
|
:param deterministic: Whether to use deterministic or stochastic actions
|
|
:param render: Whether to render the eval env or not
|
|
:return: Number of timesteps since last evaluation
|
|
"""
|
|
if 0 < eval_freq <= timesteps_since_eval and eval_env is not None:
|
|
timesteps_since_eval %= eval_freq
|
|
# Synchronise the normalization stats if needed
|
|
sync_envs_normalization(self.env, eval_env)
|
|
mean_reward, std_reward = evaluate_policy(self, eval_env, n_eval_episodes,
|
|
render=render, deterministic=deterministic)
|
|
if self.verbose > 0:
|
|
print(f"Eval num_timesteps={self.num_timesteps}, "
|
|
f"episode_reward={mean_reward:.2f} +/- {std_reward:.2f}")
|
|
print(f"FPS: {self.num_timesteps / (time.time() - self.start_time):.2f}")
|
|
return timesteps_since_eval
|