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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>
49 lines
2.4 KiB
ReStructuredText
49 lines
2.4 KiB
ReStructuredText
RL Algorithms
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=============
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This table displays the rl algorithms that are implemented in the Stable Baselines3 project,
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along with some useful characteristics: support for discrete/continuous actions, multiprocessing.
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============ =========== ============ ================= =============== ================
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Name ``Box`` ``Discrete`` ``MultiDiscrete`` ``MultiBinary`` Multi Processing
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============ =========== ============ ================= =============== ================
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A2C ✔️ ✔️ ✔️ ✔️ ✔️
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PPO ✔️ ✔️ ✔️ ✔️ ✔️
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SAC ✔️ ❌ ❌ ❌ ❌
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TD3 ✔️ ❌ ❌ ❌ ❌
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DQN ❌ ✔️ ❌ ❌ ❌
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============ =========== ============ ================= =============== ================
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.. note::
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Non-array spaces such as ``Dict`` or ``Tuple`` are not currently supported by any algorithm.
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Actions ``gym.spaces``:
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- ``Box``: A N-dimensional box that contains every point in the action
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space.
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- ``Discrete``: A list of possible actions, where each timestep only
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one of the actions can be used.
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- ``MultiDiscrete``: A list of possible actions, where each timestep only one action of each discrete set can be used.
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- ``MultiBinary``: A list of possible actions, where each timestep any of the actions can be used in any combination.
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.. note::
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Some logging values (like ``ep_rew_mean``, ``ep_len_mean``) are only available when using a ``Monitor`` wrapper
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See `Issue #339 <https://github.com/hill-a/stable-baselines/issues/339>`_ for more info.
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Reproducibility
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---------------
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Completely reproducible results are not guaranteed across Tensorflow releases or different platforms.
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Furthermore, results need not be reproducible between CPU and GPU executions, even when using identical seeds.
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In order to make computations deterministics, on your specific problem on one specific platform,
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you need to pass a ``seed`` argument at the creation of a model.
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If you pass an environment to the model using ``set_env()``, then you also need to seed the environment first.
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Credit: part of the *Reproducibility* section comes from `PyTorch Documentation <https://pytorch.org/docs/stable/notes/randomness.html>`_
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