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https://github.com/saymrwulf/stable-baselines3.git
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96b771f24e
* 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>
52 lines
1.4 KiB
Python
52 lines
1.4 KiB
Python
import gym
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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.vec_env import DummyVecEnv
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MODEL_LIST = [
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PPO,
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A2C,
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TD3,
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SAC,
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DQN,
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]
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@pytest.mark.parametrize("model_class", MODEL_LIST)
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def test_auto_wrap(model_class):
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# test auto wrapping of env into a VecEnv
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# Use different environment for DQN
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if model_class is DQN:
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env_name = 'CartPole-v0'
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else:
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env_name = 'Pendulum-v0'
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env = gym.make(env_name)
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eval_env = gym.make(env_name)
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model = model_class('MlpPolicy', env)
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model.learn(100, eval_env=eval_env)
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@pytest.mark.parametrize("model_class", MODEL_LIST)
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@pytest.mark.parametrize("env_id", ['Pendulum-v0', 'CartPole-v1'])
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def test_predict(model_class, env_id):
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if env_id == 'CartPole-v1':
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if model_class in [SAC, TD3]:
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return
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elif model_class in [DQN]:
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return
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# test detection of different shapes by the predict method
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model = model_class('MlpPolicy', env_id)
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env = gym.make(env_id)
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vec_env = DummyVecEnv([lambda: gym.make(env_id), lambda: gym.make(env_id)])
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obs = env.reset()
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action, _ = model.predict(obs)
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assert action.shape == env.action_space.shape
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assert env.action_space.contains(action)
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vec_env_obs = vec_env.reset()
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action, _ = model.predict(vec_env_obs)
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assert action.shape[0] == vec_env_obs.shape[0]
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