mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-16 21:10:08 +00:00
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>
36 lines
1.2 KiB
Python
36 lines
1.2 KiB
Python
import pytest
|
|
|
|
from stable_baselines3 import A2C, PPO, SAC, TD3, DQN
|
|
from stable_baselines3.common.noise import NormalActionNoise
|
|
|
|
N_STEPS_TRAINING = 3000
|
|
SEED = 0
|
|
|
|
|
|
@pytest.mark.parametrize("algo", [A2C, DQN, PPO, SAC, TD3])
|
|
def test_deterministic_training_common(algo):
|
|
results = [[], []]
|
|
rewards = [[], []]
|
|
# Smaller network
|
|
kwargs = {'policy_kwargs': dict(net_arch=[64])}
|
|
if algo in [TD3, SAC]:
|
|
env_id = 'Pendulum-v0'
|
|
kwargs.update({'action_noise': NormalActionNoise(0.0, 0.1),
|
|
'learning_starts': 100})
|
|
else:
|
|
env_id = 'CartPole-v1'
|
|
if algo == DQN:
|
|
kwargs.update({'learning_starts': 100})
|
|
|
|
for i in range(2):
|
|
model = algo('MlpPolicy', env_id, seed=SEED, **kwargs)
|
|
model.learn(N_STEPS_TRAINING)
|
|
env = model.get_env()
|
|
obs = env.reset()
|
|
for _ in range(100):
|
|
action, _ = model.predict(obs, deterministic=False)
|
|
obs, reward, _, _ = env.step(action)
|
|
results[i].append(action)
|
|
rewards[i].append(reward)
|
|
assert sum(results[0]) == sum(results[1]), results
|
|
assert sum(rewards[0]) == sum(rewards[1]), rewards
|