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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>
105 lines
2.4 KiB
ReStructuredText
105 lines
2.4 KiB
ReStructuredText
.. Stable Baselines3 documentation master file, created by
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sphinx-quickstart on Thu Sep 26 11:06:54 2019.
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You can adapt this file completely to your liking, but it should at least
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contain the root `toctree` directive.
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Welcome to Stable Baselines3 docs! - RL Baselines Made Easy
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===========================================================
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`Stable Baselines3 <https://github.com/DLR-RM/stable-baselines3>`_ is a set of improved implementations of reinforcement learning algorithms in PyTorch.
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It is the next major version of `Stable Baselines <https://github.com/hill-a/stable-baselines>`_.
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Github repository: https://github.com/DLR-RM/stable-baselines3
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RL Baselines3 Zoo (collection of pre-trained agents): https://github.com/DLR-RM/rl-baselines3-zoo
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RL Baselines3 Zoo also offers a simple interface to train, evaluate agents and do hyperparameter tuning.
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Main Features
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--------------
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- Unified structure for all algorithms
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- PEP8 compliant (unified code style)
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- Documented functions and classes
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- Tests, high code coverage and type hints
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- Clean code
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- Tensorboard support
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.. toctree::
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:maxdepth: 2
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:caption: User Guide
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guide/install
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guide/quickstart
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guide/rl_tips
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guide/rl
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guide/algos
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guide/examples
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guide/vec_envs
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guide/custom_env
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guide/custom_policy
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guide/callbacks
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guide/tensorboard
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guide/rl_zoo
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guide/migration
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guide/checking_nan
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guide/developer
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.. toctree::
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:maxdepth: 1
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:caption: RL Algorithms
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modules/base
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modules/a2c
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modules/ppo
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modules/sac
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modules/td3
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modules/dqn
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.. toctree::
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:maxdepth: 1
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:caption: Common
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common/atari_wrappers
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common/cmd_util
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common/distributions
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common/evaluation
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common/env_checker
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common/monitor
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common/logger
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common/noise
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common/utils
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.. toctree::
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:maxdepth: 1
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:caption: Misc
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misc/changelog
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misc/projects
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Citing Stable Baselines3
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------------------------
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To cite this project in publications:
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.. code-block:: bibtex
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@misc{stable-baselines3,
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author = {Raffin, Antonin and Hill, Ashley and Ernestus, Maximilian and Gleave, Adam and Kanervisto, Anssi and Dormann, Noah},
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title = {Stable Baselines3},
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year = {2019},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/DLR-RM/stable-baselines3}},
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}
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Indices and tables
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-------------------
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* :ref:`genindex`
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* :ref:`search`
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* :ref:`modindex`
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