"""Abstract base classes for RL algorithms."""
import io
import pathlib
import time
from abc import ABC, abstractmethod
from collections import deque
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type, Union
import gym
import numpy as np
import torch as th
from stable_baselines3.common import logger, utils
from stable_baselines3.common.callbacks import BaseCallback, CallbackList, ConvertCallback, EvalCallback
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.noise import ActionNoise
from stable_baselines3.common.policies import BasePolicy, get_policy_from_name
from stable_baselines3.common.preprocessing import is_image_space
from stable_baselines3.common.save_util import load_from_zip_file, recursive_getattr, recursive_setattr, save_to_zip_file
from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback
from stable_baselines3.common.utils import (
check_for_correct_spaces,
get_device,
get_schedule_fn,
set_random_seed,
update_learning_rate,
)
from stable_baselines3.common.vec_env import DummyVecEnv, VecEnv, VecNormalize, VecTransposeImage, unwrap_vec_normalize
def maybe_make_env(env: Union[GymEnv, str, None], monitor_wrapper: bool, verbose: int) -> Optional[GymEnv]:
"""If env is a string, make the environment; otherwise, return env.
:param env: (Union[GymEnv, str, None]) The environment to learn from.
:param monitor_wrapper: (bool) Whether to wrap env in a Monitor when creating env.
:param verbose: (int) logging verbosity
:return A Gym (vector) environment.
"""
if isinstance(env, str):
if verbose >= 1:
print(f"Creating environment from the given name '{env}'")
env = gym.make(env)
if monitor_wrapper:
env = Monitor(env, filename=None)
return env
[docs]class BaseAlgorithm(ABC):
"""
The base of RL algorithms
:param policy: (Type[BasePolicy]) Policy object
:param env: (Union[GymEnv, str, None]) The environment to learn from
(if registered in Gym, can be str. Can be None for loading trained models)
:param policy_base: (Type[BasePolicy]) The base policy used by this method
:param learning_rate: (float or callable) learning rate for the optimizer,
it can be a function of the current progress remaining (from 1 to 0)
:param policy_kwargs: (Dict[str, Any]) Additional arguments to be passed to the policy on creation
:param tensorboard_log: (str) the log location for tensorboard (if None, no logging)
:param verbose: (int) The verbosity level: 0 none, 1 training information, 2 debug
:param device: (Union[th.device, str]) Device on which the code should run.
By default, it will try to use a Cuda compatible device and fallback to cpu
if it is not possible.
:param support_multi_env: (bool) Whether the algorithm supports training
with multiple environments (as in A2C)
:param create_eval_env: (bool) Whether to create a second environment that will be
used for evaluating the agent periodically. (Only available when passing string for the environment)
:param monitor_wrapper: (bool) When creating an environment, whether to wrap it
or not in a Monitor wrapper.
:param seed: (Optional[int]) Seed for the pseudo random generators
:param use_sde: (bool) Whether to use generalized State Dependent Exploration (gSDE)
instead of action noise exploration (default: False)
:param sde_sample_freq: (int) Sample a new noise matrix every n steps when using gSDE
Default: -1 (only sample at the beginning of the rollout)
"""
def __init__(
self,
policy: Type[BasePolicy],
env: Union[GymEnv, str, None],
policy_base: Type[BasePolicy],
learning_rate: Union[float, Callable],
policy_kwargs: Dict[str, Any] = None,
tensorboard_log: Optional[str] = None,
verbose: int = 0,
device: Union[th.device, str] = "auto",
support_multi_env: bool = False,
create_eval_env: bool = False,
monitor_wrapper: bool = True,
seed: Optional[int] = None,
use_sde: bool = False,
sde_sample_freq: int = -1,
):
if isinstance(policy, str) and policy_base is not None:
self.policy_class = get_policy_from_name(policy_base, policy)
else:
self.policy_class = policy
self.device = get_device(device)
if verbose > 0:
print(f"Using {self.device} device")
self.env = None # type: Optional[GymEnv]
# get VecNormalize object if needed
self._vec_normalize_env = unwrap_vec_normalize(env)
self.verbose = verbose
self.policy_kwargs = {} if policy_kwargs is None else policy_kwargs
self.observation_space = None # type: Optional[gym.spaces.Space]
self.action_space = None # type: Optional[gym.spaces.Space]
self.n_envs = None
self.num_timesteps = 0
# Used for updating schedules
self._total_timesteps = 0
self.eval_env = None
self.seed = seed
self.action_noise = None # type: Optional[ActionNoise]
self.start_time = None
self.policy = None
self.learning_rate = learning_rate
self.tensorboard_log = tensorboard_log
self.lr_schedule = None # type: Optional[Callable]
self._last_obs = None # type: Optional[np.ndarray]
self._last_dones = None # type: Optional[np.ndarray]
# When using VecNormalize:
self._last_original_obs = None # type: Optional[np.ndarray]
self._episode_num = 0
# Used for gSDE only
self.use_sde = use_sde
self.sde_sample_freq = sde_sample_freq
# Track the training progress remaining (from 1 to 0)
# this is used to update the learning rate
self._current_progress_remaining = 1
# Buffers for logging
self.ep_info_buffer = None # type: Optional[deque]
self.ep_success_buffer = None # type: Optional[deque]
# For logging
self._n_updates = 0 # type: int
# Create and wrap the env if needed
if env is not None:
if isinstance(env, str):
if create_eval_env:
self.eval_env = maybe_make_env(env, monitor_wrapper, self.verbose)
env = maybe_make_env(env, monitor_wrapper, self.verbose)
env = self._wrap_env(env)
self.observation_space = env.observation_space
self.action_space = env.action_space
self.n_envs = env.num_envs
self.env = env
if not support_multi_env and self.n_envs > 1:
raise ValueError(
"Error: the model does not support multiple envs; it requires " "a single vectorized environment."
)
if self.use_sde and not isinstance(self.observation_space, gym.spaces.Box):
raise ValueError("generalized State-Dependent Exploration (gSDE) can only be used with continuous actions.")
def _wrap_env(self, env: GymEnv) -> VecEnv:
if not isinstance(env, VecEnv):
if self.verbose >= 1:
print("Wrapping the env in a DummyVecEnv.")
env = DummyVecEnv([lambda: env])
if is_image_space(env.observation_space) and not isinstance(env, VecTransposeImage):
if self.verbose >= 1:
print("Wrapping the env in a VecTransposeImage.")
env = VecTransposeImage(env)
return env
@abstractmethod
def _setup_model(self) -> None:
"""Create networks, buffer and optimizers."""
def _get_eval_env(self, eval_env: Optional[GymEnv]) -> Optional[GymEnv]:
"""
Return the environment that will be used for evaluation.
:param eval_env: (Optional[GymEnv]))
:return: (Optional[GymEnv])
"""
if eval_env is None:
eval_env = self.eval_env
if eval_env is not None:
eval_env = self._wrap_env(eval_env)
assert eval_env.num_envs == 1
return eval_env
def _setup_lr_schedule(self) -> None:
"""Transform to callable if needed."""
self.lr_schedule = get_schedule_fn(self.learning_rate)
def _update_current_progress_remaining(self, num_timesteps: int, total_timesteps: int) -> None:
"""
Compute current progress remaining (starts from 1 and ends to 0)
:param num_timesteps: current number of timesteps
:param total_timesteps:
"""
self._current_progress_remaining = 1.0 - float(num_timesteps) / float(total_timesteps)
def _update_learning_rate(self, optimizers: Union[List[th.optim.Optimizer], th.optim.Optimizer]) -> None:
"""
Update the optimizers learning rate using the current learning rate schedule
and the current progress remaining (from 1 to 0).
:param optimizers: (Union[List[th.optim.Optimizer], th.optim.Optimizer])
An optimizer or a list of optimizers.
"""
# Log the current learning rate
logger.record("train/learning_rate", self.lr_schedule(self._current_progress_remaining))
if not isinstance(optimizers, list):
optimizers = [optimizers]
for optimizer in optimizers:
update_learning_rate(optimizer, self.lr_schedule(self._current_progress_remaining))
[docs] def get_env(self) -> Optional[VecEnv]:
"""
Returns the current environment (can be None if not defined).
:return: (Optional[VecEnv]) The current environment
"""
return self.env
[docs] def get_vec_normalize_env(self) -> Optional[VecNormalize]:
"""
Return the ``VecNormalize`` wrapper of the training env
if it exists.
:return: Optional[VecNormalize] The ``VecNormalize`` env.
"""
return self._vec_normalize_env
[docs] def set_env(self, env: GymEnv) -> None:
"""
Checks the validity of the environment, and if it is coherent, set it as the current environment.
Furthermore wrap any non vectorized env into a vectorized
checked parameters:
- observation_space
- action_space
:param env: The environment for learning a policy
"""
check_for_correct_spaces(env, self.observation_space, self.action_space)
# it must be coherent now
# if it is not a VecEnv, make it a VecEnv
env = self._wrap_env(env)
self.n_envs = env.num_envs
self.env = env
[docs] def get_torch_variables(self) -> Tuple[List[str], List[str]]:
"""
Get the name of the torch variables that will be saved.
``th.save`` and ``th.load`` will be used with the right device
instead of the default pickling strategy.
:return: (Tuple[List[str], List[str]])
name of the variables with state dicts to save, name of additional torch tensors,
"""
state_dicts = ["policy"]
return state_dicts, []
[docs] @abstractmethod
def learn(
self,
total_timesteps: int,
callback: MaybeCallback = None,
log_interval: int = 100,
tb_log_name: str = "run",
eval_env: Optional[GymEnv] = None,
eval_freq: int = -1,
n_eval_episodes: int = 5,
eval_log_path: Optional[str] = None,
reset_num_timesteps: bool = True,
) -> "BaseAlgorithm":
"""
Return a trained model.
:param total_timesteps: (int) The total number of samples (env steps) to train on
:param callback: (MaybeCallback) callback(s) called at every step with state of the algorithm.
:param log_interval: (int) The number of timesteps before logging.
:param tb_log_name: (str) the name of the run for TensorBoard logging
:param eval_env: (gym.Env) Environment that will be used to evaluate the agent
:param eval_freq: (int) Evaluate the agent every ``eval_freq`` timesteps (this may vary a little)
:param n_eval_episodes: (int) Number of episode to evaluate the agent
:param eval_log_path: (Optional[str]) Path to a folder where the evaluations will be saved
:param reset_num_timesteps: (bool) whether or not to reset the current timestep number (used in logging)
:return: (BaseAlgorithm) the trained model
"""
[docs] def predict(
self,
observation: np.ndarray,
state: Optional[np.ndarray] = None,
mask: Optional[np.ndarray] = None,
deterministic: bool = False,
) -> Tuple[np.ndarray, Optional[np.ndarray]]:
"""
Get the model's action(s) from an observation
:param observation: (np.ndarray) the input observation
:param state: (Optional[np.ndarray]) The last states (can be None, used in recurrent policies)
:param mask: (Optional[np.ndarray]) The last masks (can be None, used in recurrent policies)
:param deterministic: (bool) Whether or not to return deterministic actions.
:return: (Tuple[np.ndarray, Optional[np.ndarray]]) the model's action and the next state
(used in recurrent policies)
"""
return self.policy.predict(observation, state, mask, deterministic)
[docs] @classmethod
def load(cls, load_path: str, env: Optional[GymEnv] = None, **kwargs) -> "BaseAlgorithm":
"""
Load the model from a zip-file
:param load_path: the location of the saved data
:param env: the new environment to run the loaded model on
(can be None if you only need prediction from a trained model) has priority over any saved environment
:param kwargs: extra arguments to change the model when loading
"""
data, params, tensors = load_from_zip_file(load_path)
if "policy_kwargs" in data:
for arg_to_remove in ["device"]:
if arg_to_remove in data["policy_kwargs"]:
del data["policy_kwargs"][arg_to_remove]
if "policy_kwargs" in kwargs and kwargs["policy_kwargs"] != data["policy_kwargs"]:
raise ValueError(
f"The specified policy kwargs do not equal the stored policy kwargs."
f"Stored kwargs: {data['policy_kwargs']}, specified kwargs: {kwargs['policy_kwargs']}"
)
# check if observation space and action space are part of the saved parameters
if "observation_space" not in data or "action_space" not in data:
raise KeyError("The observation_space and action_space were not given, can't verify new environments")
# check if given env is valid
if env is not None:
check_for_correct_spaces(env, data["observation_space"], data["action_space"])
# if no new env was given use stored env if possible
if env is None and "env" in data:
env = data["env"]
# noinspection PyArgumentList
model = cls(
policy=data["policy_class"],
env=env,
device="auto",
_init_setup_model=False, # pytype: disable=not-instantiable,wrong-keyword-args
)
# load parameters
model.__dict__.update(data)
model.__dict__.update(kwargs)
model._setup_model()
# put state_dicts back in place
for name in params:
attr = recursive_getattr(model, name)
attr.load_state_dict(params[name])
# put tensors back in place
if tensors is not None:
for name in tensors:
recursive_setattr(model, name, tensors[name])
# Sample gSDE exploration matrix, so it uses the right device
# see issue #44
if model.use_sde:
model.policy.reset_noise() # pytype: disable=attribute-error
return model
[docs] def set_random_seed(self, seed: Optional[int] = None) -> None:
"""
Set the seed of the pseudo-random generators
(python, numpy, pytorch, gym, action_space)
:param seed: (int)
"""
if seed is None:
return
set_random_seed(seed, using_cuda=self.device == th.device("cuda"))
self.action_space.seed(seed)
if self.env is not None:
self.env.seed(seed)
if self.eval_env is not None:
self.eval_env.seed(seed)
def _init_callback(
self,
callback: MaybeCallback,
eval_env: Optional[VecEnv] = None,
eval_freq: int = 10000,
n_eval_episodes: int = 5,
log_path: Optional[str] = None,
) -> BaseCallback:
"""
:param callback: (MaybeCallback) Callback(s) called at every step with state of the algorithm.
:param eval_freq: (Optional[int]) How many steps between evaluations; if None, do not evaluate.
:param n_eval_episodes: (int) How many episodes to play per evaluation
:param n_eval_episodes: (int) Number of episodes to rollout during evaluation.
:param log_path: (Optional[str]) Path to a folder where the evaluations will be saved
:return: (BaseCallback) A hybrid callback calling `callback` and performing evaluation.
"""
# Convert a list of callbacks into a callback
if isinstance(callback, list):
callback = CallbackList(callback)
# Convert functional callback to object
if not isinstance(callback, BaseCallback):
callback = ConvertCallback(callback)
# Create eval callback in charge of the evaluation
if eval_env is not None:
eval_callback = EvalCallback(
eval_env,
best_model_save_path=log_path,
log_path=log_path,
eval_freq=eval_freq,
n_eval_episodes=n_eval_episodes,
)
callback = CallbackList([callback, eval_callback])
callback.init_callback(self)
return callback
def _setup_learn(
self,
total_timesteps: int,
eval_env: Optional[GymEnv],
callback: MaybeCallback = None,
eval_freq: int = 10000,
n_eval_episodes: int = 5,
log_path: Optional[str] = None,
reset_num_timesteps: bool = True,
tb_log_name: str = "run",
) -> Tuple[int, BaseCallback]:
"""
Initialize different variables needed for training.
:param total_timesteps: (int) The total number of samples (env steps) to train on
:param eval_env: (Optional[VecEnv]) Environment to use for evaluation.
:param callback: (MaybeCallback) Callback(s) called at every step with state of the algorithm.
:param eval_freq: (int) How many steps between evaluations
:param n_eval_episodes: (int) How many episodes to play per evaluation
:param log_path: (Optional[str]) Path to a folder where the evaluations will be saved
:param reset_num_timesteps: (bool) Whether to reset or not the ``num_timesteps`` attribute
:param tb_log_name: (str) the name of the run for tensorboard log
:return: (Tuple[int, BaseCallback])
"""
self.start_time = time.time()
if self.ep_info_buffer is None or reset_num_timesteps:
# Initialize buffers if they don't exist, or reinitialize if resetting counters
self.ep_info_buffer = deque(maxlen=100)
self.ep_success_buffer = deque(maxlen=100)
if self.action_noise is not None:
self.action_noise.reset()
if reset_num_timesteps:
self.num_timesteps = 0
self._episode_num = 0
else:
# Make sure training timesteps are ahead of the internal counter
total_timesteps += self.num_timesteps
self._total_timesteps = total_timesteps
# Avoid resetting the environment when calling ``.learn()`` consecutive times
if reset_num_timesteps or self._last_obs is None:
self._last_obs = self.env.reset()
self._last_dones = np.zeros((self.env.num_envs,), dtype=np.bool)
# Retrieve unnormalized observation for saving into the buffer
if self._vec_normalize_env is not None:
self._last_original_obs = self._vec_normalize_env.get_original_obs()
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)
# Configure logger's outputs
utils.configure_logger(self.verbose, self.tensorboard_log, tb_log_name, reset_num_timesteps)
# Create eval callback if needed
callback = self._init_callback(callback, eval_env, eval_freq, n_eval_episodes, log_path)
return total_timesteps, callback
def _update_info_buffer(self, infos: List[Dict[str, Any]], dones: Optional[np.ndarray] = None) -> None:
"""
Retrieve reward and episode length and update the buffer
if using Monitor wrapper.
:param infos: ([dict])
"""
if dones is None:
dones = np.array([False] * len(infos))
for idx, info in enumerate(infos):
maybe_ep_info = info.get("episode")
maybe_is_success = info.get("is_success")
if maybe_ep_info is not None:
self.ep_info_buffer.extend([maybe_ep_info])
if maybe_is_success is not None and dones[idx]:
self.ep_success_buffer.append(maybe_is_success)
[docs] 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"]
[docs] def save(
self,
path: Union[str, pathlib.Path, io.BufferedIOBase],
exclude: Optional[Iterable[str]] = None,
include: Optional[Iterable[str]] = None,
) -> None:
"""
Save all the attributes of the object and the model parameters in a zip-file.
:param (Union[str, pathlib.Path, io.BufferedIOBase]): 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()
# Exclude is union of specified parameters (if any) and standard exclusions
if exclude is None:
exclude = []
exclude = set(exclude).union(self.excluded_save_params())
# Do not exclude params if they are specifically included
if include is not None:
exclude = exclude.difference(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.add(var_name)
# Remove parameter entries of parameters which are to be excluded
for param_name in exclude:
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()
save_to_zip_file(path, data=data, params=params_to_save, tensors=tensors)