Shark - Reinforcement Learning (rl) Project with Pytorch

Shark is inspired by several rl-platform https://github.com/thu-ml/tianshou and https://github.com/astooke/rlpyt and https://github.com/openai/baselines/ and many independent rl-algorithm implementations (e.g. https://github.com/fanchenyou/RL-study/) in github.

Implemented Algorithms

Replay Buffers supporting DQN, uniform replay, prioritized experience replay

Policy Gradient A2C, PPO.

Q-Function Policy Gradient DDPG, TD3, SAC.

Features

• Provide a fast (cpp-version) implementation of prioritized experience replay buffer. (see also https://github.com/7starsea/Prioritized-Experience-Replay)
• Provide envs: CatchBall and Game2048, see shark/example/env

Requirements

• python3, (tested on 3.6, 3.7)
• pytorch 1.2
• gym 0.15.0
• tqdm
• scikit-build, (tested on 0.10.0)
• pybind11, (tested on 2.4.3)
• cmake3, (tested on 3.14)

(we use ananconda enviroment for testing)

Compile [tested on ubuntu]

on linux, clone a copy by git clone https://github.com/7starsea/shark.git, and install with

cd shark
python setup.py install

you can also install a local copy (mainly for building cpp) using ./compile.sh.

test

cd test
mkdir -p logs params  ## for local install, also need to add a symbolic link: ln -s ../shark shark
python test_catchball -p dqn
python test_catchball -p td3
python test_atari.py -p a2c

Contributing

Shark is still in infancy, we welcome contributions from everywhere to make Shark better.