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Alok Singh
f795173b51
* Add flake8 to Travis * Add flake8-comprehensions [flake8 plugin](https://github.com/adamchainz/flake8-comprehensions) that checks for useless constructions. * Use generators instead of lists where appropriate A lot of the builtins can take in generators instead of lists. This commit applies `flake8-comprehensions` to find them. * Fix lint error * Fix some string formatting The rest can be fixed in another PR * Fix compound literals syntax This should probably be merged after #1963. * dict() -> {} * Use dict literal syntax dict(...) -> {...} * Rewrite nested dicts * Fix hanging indent * Add missing import * Add missing quote * fmt * Add missing whitespace * rm duplicate pip install This is already installed in another file. * Fix indent * move `merge_dicts` into utils * Bring up to date with `master` * Add automatic syntax upgrade * rm pyupgrade In case users want to still use it on their own, the upgrade-syn.sh script was left in the `.travis` dir.
Ray RLlib: Scalable Reinforcement Learning ========================================== Ray RLlib is an RL execution toolkit built on the Ray distributed execution framework. See the `user documentation <http://ray.readthedocs.io/en/latest/rllib.html>`__ and `paper <https://arxiv.org/abs/1712.09381>`__. RLlib includes the following reference algorithms: - Proximal Policy Optimization (`PPO <https://github.com/ray-project/ray/tree/master/python/ray/rllib/ppo>`__) which is a proximal variant of `TRPO <https://arxiv.org/abs/1502.05477>`__. - Policy Gradients (`PG <https://github.com/ray-project/ray/tree/master/python/ray/rllib/pg>`__). - Asynchronous Advantage Actor-Critic (`A3C <https://github.com/ray-project/ray/tree/master/python/ray/rllib/a3c>`__). - Deep Q Networks (`DQN <https://github.com/ray-project/ray/tree/master/python/ray/rllib/dqn>`__). - Deep Deterministic Policy Gradients (`DDPG <https://github.com/ray-project/ray/tree/master/python/ray/rllib/ddpg>`__, `DDPG2 <https://github.com/ray-project/ray/tree/master/python/ray/rllib/ddpg2>`__). - Ape-X Distributed Prioritized Experience Replay, including both `DQN <https://github.com/ray-project/ray/blob/master/python/ray/rllib/dqn/apex.py>`__ and `DDPG <https://github.com/ray-project/ray/blob/master/python/ray/rllib/ddpg/apex.py>`__ variants. - Evolution Strategies (`ES <https://github.com/ray-project/ray/tree/master/python/ray/rllib/es>`__), as described in `this paper <https://arxiv.org/abs/1703.03864>`__. These algorithms can be run on any OpenAI Gym MDP, including custom ones written and registered by the user.