* Update doc and add new example
* Add save/load replay buffer example
* Add save format + export doc
* Add example for get/set parameters
* Typos and minor edits
* Add results sections
* Add note about performance
* Add DDPG results
* Address comments
* Fix grammar/wording
Co-authored-by: Anssi "Miffyli" Kanervisto <kaneran21@hotmail.com>
* Added working her version, Online sampling is missing.
* Updated test_her.
* Added first version of online her sampling. Still problems with tensor dimensions.
* Reformat
* Fixed tests
* Added some comments.
* Updated changelog.
* Add missing init file
* Fixed some small bugs.
* Reduced arguments for HER, small changes.
* Added getattr. Fixed bug for online sampling.
* Updated save/load funtions. Small changes.
* Added her to init.
* Updated save method.
* Updated her ratio.
* Move obs_wrapper
* Added DQN test.
* Fix potential bug
* Offline and online her share same sample_goal function.
* Changed lists into arrays.
* Updated her test.
* Fix online sampling
* Fixed action bug. Updated time limit for episodes.
* Updated convert_dict method to take keys as arguments.
* Renamed obs dict wrapper.
* Seed bit flipping env
* Remove get_episode_dict
* Add fast online sampling version
* Added documentation.
* Vectorized reward computation
* Vectorized goal sampling
* Update time limit for episodes in online her sampling.
* Fix max episode length inference
* Bug fix for Fetch envs
* Fix for HER + gSDE
* Reformat (new black version)
* Added info dict to compute new reward. Check her_replay_buffer again.
* Fix info buffer
* Updated done flag.
* Fixes for gSDE
* Offline her version uses now HerReplayBuffer as episode storage.
* Fix num_timesteps computation
* Fix get torch params
* Vectorized version for offline sampling.
* Modified offline her sampling to use sample method of her_replay_buffer
* Updated HER tests.
* Updated documentation
* Cleanup docstrings
* Updated to review comments
* Fix pytype
* Update according to review comments.
* Removed random goal strategy. Updated sample transitions.
* Updated migration. Removed time signal removal.
* Update doc
* Fix potential load issue
* Add VecNormalize support for dict obs
* Updated saving/loading replay buffer for HER.
* Fix test memory usage
* Fixed save/load replay buffer.
* Fixed save/load replay buffer
* Fixed transition index after loading replay buffer in online sampling
* Better error handling
* Add tests for get_time_limit
* More tests for VecNormalize with dict obs
* Update doc
* Improve HER description
* Add test for sde support
* Add comments
* Add comments
* Remove check that was always valid
* Fix for terminal observation
* Updated buffer size in offline version and reset of HER buffer
* Reformat
* Update doc
* Remove np.empty + add doc
* Fix loading
* Updated loading replay buffer
* Separate online and offline sampling + bug fixes
* Update tensorboard log name
* Version bump
* Bug fix for special case
Co-authored-by: Antonin Raffin <antonin.raffin@dlr.de>
Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
* Fix storing correct episode dones
* Fix number of filters in NatureCNN network
* Add TF-like RMSprop for matching performance with sb2
* Remove stuff that was accidentally included
* Reformat
* Clarify variable naming
* Update changelog
* Add comment on RMSprop implementations to A2C
* Add test for RMSpropTFLike
Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
* Add DDPG + TD3 with any number of critics
* Allow any number of critics for SAC
* Update doc
* [ci skip] Update DDPG example
* Remove unused parameter
* Add DDPG to identity test
* Fix computation with n_critics=1,3
* Update doc
* Apply suggestions from code review
Co-authored-by: Adam Gleave <adam@gleave.me>
* Update docstrings for off-policy algos
* Add check for sde
Co-authored-by: Adam Gleave <adam@gleave.me>
* 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>
* Split torch module code into torch_layers file
* Updated reference to CNN
* Change 'CxWxH' to 'CxHxW', as per common notion
* Fix missing import in policies.py
* Move PPOPolicy to OnlineActorCriticPolicy
* Create OnPolicyRLModel from PPO, and make A2C and PPO inherit
* Update A2C optimizer comment
* Clean weight init scales for clarity
* Fix A2C log_interval default parameter
* Rename 'progress' to 'progress_remaining
* Rename 'Models' to 'Algorithms'
* Rename 'OnlineActorCriticPolicy' to 'ActorCriticPolicy'
* Move static functions out from BaseAlgorithm
* Move on/off_policy base algorithms to their own files
* Add files for A2C/PPO
* Fix docs
* Fix pytype
* Update documentation on OnPolicyAlgorithm
* Add proper doctstring for on_policy rollout gathering
* Add bit clarification on the mlppolicy/cnnpolicy naming
* Move static function is_vectorized_policies to utils.py
* Checking docstrings, pep8 fixes
* Update changelog
* Clean changelog
* Remove policy warnings for sac/td3
* Add monitor_wrapper for OnPolicyAlgorithm. Clean tb logging variables. Add parameter keywords to OffPolicyAlgorithm super init
Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
