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https://github.com/saymrwulf/stable-baselines3.git
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* 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>
59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
import numpy as np
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import pytest
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from stable_baselines3 import A2C, PPO, SAC, TD3, DQN
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from stable_baselines3.common.identity_env import (IdentityEnvBox, IdentityEnv,
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IdentityEnvMultiBinary, IdentityEnvMultiDiscrete)
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from stable_baselines3.common.vec_env import DummyVecEnv
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from stable_baselines3.common.evaluation import evaluate_policy
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from stable_baselines3.common.noise import NormalActionNoise
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DIM = 4
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@pytest.mark.parametrize("model_class", [A2C, PPO, DQN])
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@pytest.mark.parametrize("env", [IdentityEnv(DIM), IdentityEnvMultiDiscrete(DIM), IdentityEnvMultiBinary(DIM)])
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def test_discrete(model_class, env):
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env_ = DummyVecEnv([lambda: env])
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kwargs = {}
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n_steps = 3000
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if model_class == DQN:
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kwargs = dict(learning_starts=0)
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n_steps = 4000
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# DQN only support discrete actions
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if isinstance(env, (IdentityEnvMultiDiscrete, IdentityEnvMultiBinary)):
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return
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model = model_class('MlpPolicy', env_, gamma=0.5, seed=1, **kwargs).learn(n_steps)
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evaluate_policy(model, env_, n_eval_episodes=20, reward_threshold=90)
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obs = env.reset()
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assert np.shape(model.predict(obs)[0]) == np.shape(obs)
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@pytest.mark.parametrize("model_class", [A2C, PPO, SAC, TD3])
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def test_continuous(model_class):
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env = IdentityEnvBox(eps=0.5)
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n_steps = {
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A2C: 3500,
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PPO: 3000,
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SAC: 700,
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TD3: 500
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}[model_class]
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kwargs = dict(
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policy_kwargs=dict(net_arch=[64, 64]),
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seed=0,
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gamma=0.95
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)
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if model_class in [TD3]:
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n_actions = 1
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action_noise = NormalActionNoise(mean=np.zeros(n_actions), sigma=0.1 * np.ones(n_actions))
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kwargs['action_noise'] = action_noise
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model = model_class('MlpPolicy', env, **kwargs).learn(n_steps)
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evaluate_policy(model, env, n_eval_episodes=20, reward_threshold=90)
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