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Release 2.4.0a10 (WIP)

New algorithm: CrossQ, and better defaults for SAC/TQC on Swimmer-v4 env

Breaking Changes

  • Updated defaults hyperparameters for TQC/SAC for Swimmer-v4 (decrease gamma for more consistent results) (@JacobHA) W&B report
  • Upgraded to SB3 >= 2.4.0

New Features

  • Added CrossQ hyperparameters for SB3-contrib (@danielpalen)

Bug fixes

Documentation

Other

  • Updated PyTorch version to 2.4.1 in the CI
  • Switched to uv to download packages faster on GitHub CI

Release 2.3.0 (2024-03-31)

Breaking Changes

  • Updated defaults hyperparameters for TD3/DDPG to be more consistent with SAC
  • Upgraded MuJoCo envs hyperparameters to v4 (pre-trained agents need to be updated)
  • Upgraded to SB3 >= 2.3.0

Other

  • Added test dependencies to setup.py (@power-edge)
  • Simplify dependencies of requirements.txt (remove duplicates from setup.py)

Release 2.2.1 (2023-11-17)

Breaking Changes

  • Removed gym dependency, the package is still required for some pretrained agents.
  • Upgraded to SB3 >= 2.2.1
  • Upgraded to Huggingface-SB3 >= 3.0
  • Upgraded to pytablewriter >= 1.0

New Features

  • Added --eval-env-kwargs to train.py (@Quentin18)
  • Added ppo_lstm to hyperparams_opt.py (@technocrat13)

Bug fixes

  • Upgraded to pybullet_envs_gymnasium>=0.4.0
  • Removed old hacks (for instance limiting offpolicy algorithms to one env at test time)

Documentation

Other

  • Updated docker image, removed support for X server
  • Replaced deprecated optuna.suggest_uniform(...) by optuna.suggest_float(..., low=..., high=...)
  • Switched to ruff for sorting imports
  • Updated tests to use shlex.split()
  • Fixed rl_zoo3/hyperparams_opt.py type hints
  • Fixed rl_zoo3/exp_manager.py type hints

Release 2.1.0 (2023-08-17)

Breaking Changes

  • Dropped python 3.7 support
  • SB3 now requires PyTorch 1.13+
  • Upgraded to SB3 >= 2.1.0
  • Upgraded to Huggingface-SB3 >= 2.3
  • Upgraded to Optuna >= 3.0
  • Upgraded to cloudpickle >= 2.2.1

New Features

  • Added python 3.11 support

Bug fixes

Documentation

Other

Release 2.0.0 (2023-06-22)

Gymnasium support

Warning Stable-Baselines3 (SB3) v2.0.0 will be the last one supporting python 3.7

Breaking Changes

  • Fixed bug in HistoryWrapper, now returns the correct obs space limits
  • Upgraded to SB3 >= 2.0.0
  • Upgraded to Huggingface-SB3 >= 2.2.5
  • Upgraded to Gym API 0.26+, RL Zoo3 doesn't work anymore with Gym 0.21

New Features

  • Added Gymnasium support
  • Gym 0.26+ patches to continue working with pybullet and TimeLimit wrapper

Bug fixes

  • Renamed CarRacing-v1 to CarRacing-v2 in hyperparameters
  • Huggingface push to hub now accepts a --n-timesteps argument to adjust the length of the video
  • Fixed record_video steps (before it was stepping in a closed env)

Release 1.8.0 (2023-04-07)

New Documentation, Multi-Env HerReplayBuffer

Warning Stable-Baselines3 (SB3) v1.8.0 will be the last one to use Gym as a backend. Starting with v2.0.0, Gymnasium will be the default backend (though SB3 will have compatibility layers for Gym envs). You can find a migration guide here. If you want to try the SB3 v2.0 alpha version, you can take a look at PR #1327.

Breaking Changes

  • Upgraded to SB3 >= 1.8.0
  • Upgraded to new HerReplayBuffer implementation that supports multiple envs
  • Removed TimeFeatureWrapper for Panda and Fetch envs, as the new replay buffer should handle timeout.

New Features

  • Tuned hyperparameters for RecurrentPPO on Swimmer
  • Documentation is now built using Sphinx and hosted on read the doc
  • Added hyperparameters pre-trained agents for PPO on 11 MiniGrid envs

Bug fixes

  • Set highway-env version to 1.5 and setuptools to v65.5 for the CI
  • Removed use_auth_token for push to hub util
  • Reverted from v3 to v2 for HumanoidStandup, Reacher, InvertedPendulum and InvertedDoublePendulum since they were not part of the mujoco refactoring (see openai/gym#1304)
  • Fixed gym-minigrid policy (from MlpPolicy to MultiInputPolicy)

Documentation

Other

  • Added support for ruff (fast alternative to flake8) in the Makefile
  • Removed Gitlab CI file
  • Replaced deprecated optuna.suggest_loguniform(...) by optuna.suggest_float(..., log=True)
  • Switched to ruff and pyproject.toml
  • Removed online_sampling and max_episode_length argument when using HerReplayBuffer

Release 1.7.0 (2023-01-10)

SB3 v1.7.0, added support for python config files

Breaking Changes

  • --yaml-file argument was renamed to -conf (--conf-file) as now python file are supported too
  • Upgraded to SB3 >= 1.7.0 (changed net_arch=[dict(pi=.., vf=..)] to net_arch=dict(pi=.., vf=..))

New Features

  • Specifying custom policies in yaml file is now supported (@Rick-v-E)
  • Added monitor_kwargs parameter
  • Handle the env_kwargs of render:True under the hood for panda-gym v1 envs in enjoy replay to match visualzation behavior of other envs
  • Added support for python config file
  • Tuned hyperparameters for PPO on Swimmer
  • Added -tags/--wandb-tags argument to train.py to add tags to the wandb run
  • Added a sb3 version tag to the wandb run

Bug fixes

  • Allow python -m rl_zoo3.cli to be called directly
  • Fixed a bug where custom environments were not found despite passing --gym-package when using subprocesses
  • Fixed TRPO hyperparameters for MinitaurBulletEnv-v0, MinitaurBulletDuckEnv-v0, HumanoidBulletEnv-v0, InvertedDoublePendulumBulletEnv-v0 and InvertedPendulumSwingupBulletEnv

Documentation

Other

  • scripts/plot_train.py plots models such that newer models appear on top of older ones.
  • Added additional type checking using mypy
  • Standardized the use of from gym import spaces

Release 1.6.3 (2022-10-13)

Breaking Changes

New Features

Bug fixes

  • python3 -m rl_zoo3.train now works as expected

Documentation

  • Added instructions and examples on passing arguments in an interactive session (@richter43)

Other

  • Used issue forms instead of issue templates

Release 1.6.2.post2 (2022-10-10)

Breaking Changes

  • RL Zoo is now a python package
  • low pass filter was removed
  • Upgraded to Stable-Baselines3 (SB3) >= 1.6.2
  • Upgraded to sb3-contrib >= 1.6.2
  • Use now built-in SB3 ProgressBarCallback instead of TQDMCallback

New Features

  • RL Zoo cli: rl_zoo3 train and rl_zoo3 enjoy

Bug fixes

Documentation

Other

Release 1.6.1 (2022-09-30)

Progress bar and custom yaml file

Breaking Changes

  • Upgraded to Stable-Baselines3 (SB3) >= 1.6.1
  • Upgraded to sb3-contrib >= 1.6.1

New Features

  • Added --yaml-file argument option for train.py to read hyperparameters from custom yaml files (@JohannesUl)

Bug fixes

  • Added custom_object parameter on record_video.py (@Affonso-Gui)
  • Changed optimize_memory_usage to False for DQN/QR-DQN on record_video.py (@Affonso-Gui)
  • In ExperimentManager _maybe_normalize set training to False for eval envs, to prevent normalization stats from being updated in eval envs (e.g. in EvalCallback) (@pchalasani).
  • Only one env is used to get the action space while optimizing hyperparameters and it is correctly closed (@SammyRamone)
  • Added progress bar via the -P argument using tqdm and rich

Documentation

Other

Release 1.6.0 (2022-08-05)

RecurrentPPO (ppo_lstm) and Huggingface integration

Breaking Changes

  • Change default value for number of hyperparameter optimization trials from 10 to 500. (@ernestum)
  • Derive number of intermediate pruning evaluations from number of time steps (1 evaluation per 100k time steps.) (@ernestum)
  • Updated default --eval-freq from 10k to 25k steps
  • Update default horizon to 2 for the HistoryWrapper
  • Upgrade to Stable-Baselines3 (SB3) >= 1.6.0
  • Upgrade to sb3-contrib >= 1.6.0

New Features

  • Support setting PyTorch's device with thye --device flag (@gregwar)
  • Add --max-total-trials parameter to help with distributed optimization. (@ernestum)
  • Added vec_env_wrapper support in the config (works the same as env_wrapper)
  • Added Huggingface hub integration
  • Added RecurrentPPO support (aka ppo_lstm)
  • Added autodownload for "official" sb3 models from the hub
  • Added Humanoid-v3, Ant-v3, Walker2d-v3 models for A2C (@pseudo-rnd-thoughts)
  • Added MsPacman models

Bug fixes

  • Fix Reacher-v3 name in PPO hyperparameter file
  • Pinned ale-py==0.7.4 until new SB3 version is released
  • Fix enjoy / record videos with LSTM policy
  • Fix bug with environments that have a slash in their name (@ernestum)
  • Changed optimize_memory_usage to False for DQN/QR-DQN on Atari games, if you want to save RAM, you need to deactivate handle_timeout_termination in the replay_buffer_kwargs

Documentation

Other

  • When pruner is set to "none", use NopPruner instead of diverted MedianPruner (@qgallouedec)

Release 1.5.0 (2022-03-25)

Support for Weight and Biases experiment tracking

Breaking Changes

  • Upgrade to Stable-Baselines3 (SB3) >= 1.5.0
  • Upgrade to sb3-contrib >= 1.5.0
  • Upgraded to gym 0.21

New Features

  • Verbose mode for each trial (when doing hyperparam optimization) can now be activated using the debug mode (verbose == 2)
  • Support experiment tracking via Weights and Biases via the --track flag (@vwxyzjn)
  • Support tracking raw episodic stats via RawStatisticsCallback (@vwxyzjn, see #216)

Bug fixes

  • Policies saved during during optimization with distributed Optuna load on new systems (@jkterry)
  • Fixed script for recording video that was not up to date with the enjoy script

Documentation

Other

Release 1.4.0 (2022-01-19)

Breaking Changes

  • Dropped python 3.6 support
  • Upgrade to Stable-Baselines3 (SB3) >= 1.4.0
  • Upgrade to sb3-contrib >= 1.4.0

New Features

  • Added mujoco hyperparameters
  • Added MuJoCo pre-trained agents
  • Added script to parse best hyperparameters of an optuna study
  • Added TRPO support
  • Added ARS support and pre-trained agents

Bug fixes

Documentation

  • Replace front image

Other

Release 1.3.0 (2021-10-23)

rliable plots and bug fixes

WARNING: This version will be the last one supporting Python 3.6 (end of life in Dec 2021). We highly recommended you to upgrade to Python >= 3.7.

Breaking Changes

  • Upgrade to panda-gym 1.1.1
  • Upgrade to Stable-Baselines3 (SB3) >= 1.3.0
  • Upgrade to sb3-contrib >= 1.3.0

New Features

  • Added support for using rliable for performance comparison

Bug fixes

  • Fix training with Dict obs and channel last images

Documentation

Other

  • Updated docker image
  • constrained gym version: gym>=0.17,<0.20
  • Better hyperparameters for A2C/PPO on Pendulum

Release 1.2.0 (2021-09-08)

Breaking Changes

  • Upgrade to Stable-Baselines3 (SB3) >= 1.2.0
  • Upgrade to sb3-contrib >= 1.2.0

New Features

  • Added support for Python 3.10

Bug fixes

  • Fix --load-last-checkpoint (@SammyRamone)
  • Fix TypeError for gym.Env class entry points in ExperimentManager (@schuderer)
  • Fix usage of callbacks during hyperparameter optimization (@SammyRamone)

Documentation

Other

  • Added python 3.9 to Github CI
  • Increased DQN replay buffer size for Atari games (@nikhilrayaprolu)

Release 1.1.0 (2021-07-01)

Breaking Changes

  • Upgrade to Stable-Baselines3 (SB3) >= 1.1.0
  • Upgrade to sb3-contrib >= 1.1.0
  • Add timeout handling (cf SB3 doc)
  • HER is now a replay buffer class and no more an algorithm
  • Removed PlotNoiseRatioCallback
  • Removed PlotActionWrapper
  • Changed 'lr' key in Optuna param dict to 'learning_rate' so the dict can be directly passed to SB3 methods (@jkterry)

New Features

  • Add support for recording videos of best models and checkpoints (@mcres)
  • Add support for recording videos of training experiments (@mcres)
  • Add support for dictionary observations
  • Added experimental parallel training (with utils.callbacks.ParallelTrainCallback)
  • Added support for using multiple envs for evaluation
  • Added --load-last-checkpoint option for the enjoy script
  • Save Optuna study object at the end of hyperparameter optimization and plot the results (plotly package required)
  • Allow to pass multiple folders to scripts/plot_train.py
  • Flag to save logs and optimal policies from each training run (@jkterry)

Bug fixes

  • Fixed video rendering for PyBullet envs on Linux
  • Fixed get_latest_run_id() so it works in Windows too (@NicolasHaeffner)
  • Fixed video record when using HER replay buffer

Documentation

  • Updated README (dict obs are now supported)

Other

  • Added is_bullet() to ExperimentManager
  • Simplify close() for the enjoy script
  • Updated docker image to include latest black version
  • Updated TD3 Walker2D model (thanks @modanesh)
  • Fixed typo in plot title (@scottemmons)
  • Minimum cloudpickle version added to requirements.txt (@amy12xx)
  • Fixed atari-py version (ROM missing in newest release)
  • Updated SAC and TD3 search spaces
  • Cleanup eval_freq documentation and variable name changes (@jkterry)
  • Add clarifying print statement when printing saved hyperparameters during optimization (@jkterry)
  • Clarify n_evaluations help text (@jkterry)
  • Simplified hyperparameters files making use of defaults
  • Added new TQC+HER agents
  • Add panda-gym environments (@qgallouedec)

Release 1.0 (2021-03-17)

Breaking Changes

  • Upgrade to SB3 >= 1.0
  • Upgrade to sb3-contrib >= 1.0

New Features

  • Added 100+ trained agents + benchmark file
  • Add support for loading saved model under python 3.8+ (no retraining possible)
  • Added Robotics pre-trained agents (@sgillen)

Bug fixes

  • Bug fixes for HER handling action noise
  • Fixed double reset bug with HER and enjoy script

Documentation

  • Added doc about plotting scripts

Other

  • Updated HER hyperparameters

Pre-Release 0.11.1 (2021-02-27)

Breaking Changes

  • Removed LinearNormalActionNoise
  • Evaluation is now deterministic by default, except for Atari games
  • sb3_contrib is now required
  • TimeFeatureWrapper was moved to the contrib repo
  • Replaced old plot_train.py script with updated plot_training_success.py
  • Renamed n_episodes_rollout to train_freq tuple to match latest version of SB3

New Features

  • Added option to choose which VecEnv class to use for multiprocessing
  • Added hyperparameter optimization support for TQC
  • Added support for QR-DQN from SB3 contrib

Bug fixes

  • Improved detection of Atari games
  • Fix potential bug in plotting script when there is not enough timesteps
  • Fixed a bug when using HER + DQN/TQC for hyperparam optimization

Documentation

  • Improved documentation (@cboettig)

Other

  • Refactored train script, now uses a ExperimentManager class
  • Replaced make_env with SB3 built-in make_vec_env
  • Add more type hints (utils/utils.py done)
  • Use f-strings when possible
  • Changed PPO atari hyperparameters (removed vf clipping)
  • Changed A2C atari hyperparameters (eps value of the optimizer)
  • Updated benchmark script
  • Updated hyperparameter optim search space (commented gSDE for A2C/PPO)
  • Updated DQN hyperparameters for CartPole
  • Do not wrap channel-first image env (now natively supported by SB3)
  • Removed hack to log success rate
  • Simplify plot script

Pre-Release 0.10.0 (2020-10-28)

Breaking Changes

New Features

  • Added support for HER
  • Added low-pass filter wrappers in utils/wrappers.py
  • Added TQC support, implementation from sb3-contrib

Bug fixes

  • Fixed TimeFeatureWrapper inferring max timesteps
  • Fixed flatten_dict_observations in utils/utils.py for recent Gym versions (@ManifoldFR)
  • VecNormalize now takes gamma hyperparameter into account
  • Fix loading of VecNormalize when continuing training or using trained agent

Documentation

Other

  • Added tests for the wrappers
  • Updated plotting script

Release 0.8.0 (2020-08-04)

Breaking Changes

New Features

  • Distributed optimization (@SammyRamone)
  • Added --load-checkpoints to load particular checkpoints
  • Added --num-threads to enjoy script
  • Added DQN support
  • Added saving of command line args (@SammyRamone)
  • Added DDPG support
  • Added version
  • Added RMSpropTFLike support

Bug fixes

  • Fixed optuna warning (@SammyRamone)
  • Fixed --save-freq which was not taking parallel env into account
  • Set buffer_size to 1 when testing an Off-Policy model (e.g. SAC/DQN) to avoid memory allocation issue
  • Fixed seed at load time for enjoy.py
  • Non-deterministic eval when doing hyperparameter optimization on atari games
  • Use 'maximize' for hyperparameter optimization (@SammyRamone)
  • Fixed a bug where reward where not normalized when doing hyperparameter optimization (@caburu)
  • Removed nminibatches from ppo.yml for MountainCar-v0 and Acrobot-v1. (@blurLake)
  • Fixed --save-replay-buffer to be compatible with latest SB3 version
  • Close environment at the end of training
  • Updated DQN hyperparameters on simpler gym env (due to an update in the implementation)

Documentation

Other

  • Reformat enjoy.py, test_enjoy.py, test_hyperparams_opt.py, test_train.py, train.py, callbacks.py, hyperparams_opt.py, utils.py, wrappers.py (@salmannotkhan)
  • Reformat record_video.py (@salmannotkhan)
  • Added codestyle check make lint using flake8
  • Reformat benchmark.py (@salmannotkhan)
  • Added github ci
  • Fixes most linter warnings
  • Now using black and isort for auto-formatting
  • Updated plots