stable-baselines3/stable_baselines3/common/cmd_util.py
Noah 96b771f24e
Implement DQN (#28)
* Created DQN template according to the paper.
Next steps:
- Create Policy
- Complete Training
- Debug

* Changed Base Class

* refactor save, to be consistence with overriding the excluded_save_params function. Do not try to exclude the parameters twice.

* Added simple DQN policy

* Finished learn and train function
- missing correct loss computation

* changed collect_rollouts to work with discrete space

* moved discrete space collect_rollouts to dqn

* basic dqn working

* deleted SDE related code

* added gradient clipping and moved greedy policy to policy

* changed policy to implement target network
and added soft update(in fact standart tau is 1 so hard update)

* fixed policy setup

* rebase target_update_intervall on _n_updates

* adapted all tests
all tests passing

* Move to stable-baseline3

* Fixes for DQN

* Fix tests + add CNNPolicy

* Allow any optimizer for DQN

* added some util functions to create a arbitrary linear schedule, fixed pickle problem with old exploration schedule

* more documentation

* changed buffer dtype

* refactor and document

* Added Sphinx Documentation
Updated changelog.rst

* removed custom collect_rollouts as it is no longer necessary

* Implemented suggestions to clean code and documentation.

* extracted some functions on tests to reduce duplicated code

* added support for exploration_fraction

* Fixed exploration_fraction

* Added documentation

* Fixed get_linear_fn -> proper progress scaling

* Merged master

* Added nature reference

* Changed default parameters to https://www.nature.com/articles/nature14236/tables/1

* Fixed n_updates to be incremented correctly

* Correct train_freq

* Doc update

* added special parameter for DQN in tests

* different fix for test_discrete

* Update docs/modules/dqn.rst

Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>

* Update docs/modules/dqn.rst

Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>

* Update docs/modules/dqn.rst

Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>

* Added RMSProp in optimizer_kwargs, as described in nature paper

* Exploration fraction is inverse of 50.000.000 (total frames) / 1.000.000 (frames with linear schedule) according to nature paper

* Changelog update for buffer dtype

* standard exlude parameters should be always excluded to assure proper saving only if intentionally included by ``include`` parameter

* slightly more iterations on test_discrete to pass the test

* added param use_rms_prop instead of mutable default argument

* forgot alpha

* using huber loss, adam and learning rate 1e-4

* account for train_freq in update_target_network

* Added memory check for both buffers

* Doc updated for buffer allocation

* Added psutil Requirement

* Adapted test_identity.py

* Fixes with new SB3 version

* Fix for tensorboard name

* Convert assert to warning and fix tests

* Refactor off-policy algorithms

* Fixes

* test: remove next_obs in replay buffer

* Update changelog

* Fix tests and use tmp_path where possible

* Fix sampling bug in buffer

* Do not store next obs on episode termination

* Fix replay buffer sampling

* Update comment

* moved epsilon from policy to model

* Update predict method

* Update atari wrappers to match SB2

* Minor edit in the buffers

* Update changelog

* Merge branch 'master' into dqn

* Update DQN to new structure

* Fix tests and remove hardcoded path

* Fix for DQN

* Disable memory efficient replay buffer by default

* Fix docstring

* Add tests for memory efficient buffer

* Update changelog

* Split collect rollout

* Move target update outside `train()` for DQN

* Update changelog

* Update linear schedule doc

* Cleanup DQN code

* Minor edit

* Update version and docker images

Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
2020-06-29 11:16:54 +02:00

110 lines
5.4 KiB
Python

import os
import warnings
from typing import Dict, Any, Optional, Callable, Type, Union
import gym
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.atari_wrappers import AtariWrapper
from stable_baselines3.common.vec_env import DummyVecEnv, SubprocVecEnv
def make_vec_env(env_id: Union[str, Type[gym.Env]],
n_envs: int = 1,
seed: Optional[int] = None,
start_index: int = 0,
monitor_dir: Optional[str] = None,
wrapper_class: Optional[Callable] = None,
env_kwargs: Optional[Dict[str, Any]] = None,
vec_env_cls: Optional[Union[DummyVecEnv, SubprocVecEnv]] = None,
vec_env_kwargs: Optional[Dict[str, Any]] = None):
"""
Create a wrapped, monitored ``VecEnv``.
By default it uses a ``DummyVecEnv`` which is usually faster
than a ``SubprocVecEnv``.
:param env_id: (str or Type[gym.Env]) the environment ID or the environment class
:param n_envs: (int) the number of environments you wish to have in parallel
:param seed: (int) the initial seed for the random number generator
:param start_index: (int) start rank index
:param monitor_dir: (str) Path to a folder where the monitor files will be saved.
If None, no file will be written, however, the env will still be wrapped
in a Monitor wrapper to provide additional information about training.
:param wrapper_class: (gym.Wrapper or callable) Additional wrapper to use on the environment.
This can also be a function with single argument that wraps the environment in many things.
:param env_kwargs: (dict) Optional keyword argument to pass to the env constructor
:param vec_env_cls: (Type[VecEnv]) A custom ``VecEnv`` class constructor. Default: None.
:param vec_env_kwargs: (dict) Keyword arguments to pass to the ``VecEnv`` class constructor.
:return: (VecEnv) The wrapped environment
"""
env_kwargs = {} if env_kwargs is None else env_kwargs
vec_env_kwargs = {} if vec_env_kwargs is None else vec_env_kwargs
def make_env(rank):
def _init():
if isinstance(env_id, str):
env = gym.make(env_id)
if len(env_kwargs) > 0:
warnings.warn("No environment class was passed (only an env ID) so ``env_kwargs`` will be ignored")
else:
env = env_id(**env_kwargs)
if seed is not None:
env.seed(seed + rank)
env.action_space.seed(seed + rank)
# Wrap the env in a Monitor wrapper
# to have additional training information
monitor_path = os.path.join(monitor_dir, str(rank)) if monitor_dir is not None else None
# Create the monitor folder if needed
if monitor_path is not None:
os.makedirs(monitor_dir, exist_ok=True)
env = Monitor(env, filename=monitor_path)
# Optionally, wrap the environment with the provided wrapper
if wrapper_class is not None:
env = wrapper_class(env)
return env
return _init
# No custom VecEnv is passed
if vec_env_cls is None:
# Default: use a DummyVecEnv
vec_env_cls = DummyVecEnv
return vec_env_cls([make_env(i + start_index) for i in range(n_envs)], **vec_env_kwargs)
def make_atari_env(env_id: Union[str, Type[gym.Env]],
n_envs: int = 1,
seed: Optional[int] = None,
start_index: int = 0,
monitor_dir: Optional[str] = None,
wrapper_kwargs: Optional[Dict[str, Any]] = None,
env_kwargs: Optional[Dict[str, Any]] = None,
vec_env_cls: Optional[Union[DummyVecEnv, SubprocVecEnv]] = None,
vec_env_kwargs: Optional[Dict[str, Any]] = None):
"""
Create a wrapped, monitored VecEnv for Atari.
It is a wrapper around ``make_vec_env`` that includes common preprocessing for Atari games.
:param env_id: (str or Type[gym.Env]) the environment ID or the environment class
:param n_envs: (int) the number of environments you wish to have in parallel
:param seed: (int) the initial seed for the random number generator
:param start_index: (int) start rank index
:param monitor_dir: (str) Path to a folder where the monitor files will be saved.
If None, no file will be written, however, the env will still be wrapped
in a Monitor wrapper to provide additional information about training.
:param wrapper_kwargs: (Dict[str, Any]) Optional keyword argument to pass to the ``AtariWrapper``
:param env_kwargs: (Dict[str, Any]) Optional keyword argument to pass to the env constructor
:param vec_env_cls: (Type[VecEnv]) A custom ``VecEnv`` class constructor. Default: None.
:param vec_env_kwargs: (Dict[str, Any]) Keyword arguments to pass to the ``VecEnv`` class constructor.
:return: (VecEnv) The wrapped environment
"""
if wrapper_kwargs is None:
wrapper_kwargs = {}
def atari_wrapper(env: gym.Env) -> gym.Env:
env = AtariWrapper(env, **wrapper_kwargs)
return env
return make_vec_env(env_id, n_envs=n_envs, seed=seed, start_index=start_index,
monitor_dir=monitor_dir, wrapper_class=atari_wrapper,
env_kwargs=env_kwargs, vec_env_cls=vec_env_cls, vec_env_kwargs=vec_env_kwargs)