Deep RL Toolkit is a flexible and high-efficient reinforcement learning framework. RLToolkit is developed for practitioners with the following advantages:

• Reproducible. We provide algorithms that stably reproduce the result of many influential reinforcement learning algorithms.

• Extensible. Build new algorithms quickly by inheriting the abstract class in the framework.

• Reusable. Algorithms provided in the repository could be directly adapted to a new task by defining a forward network and training mechanism will be built automatically.

• Elastic: allows to elastically and automatically allocate computing resources on the cloud.

• Lightweight: the core codes <1,000 lines (check Demo).

• Stable: much more stable than Stable Baselines 3 by utilizing various ensemble methods.

• Deep-RL-Toolkit
  • Overview
  • Table of Content
  • Supported Algorithms
  • Supported Envs
  • Examples
    • Quick Start
  • References
    • Reference Papers
    • References code

RLToolkit implements the following model-free deep reinforcement learning (DRL) algorithms:

• OpenAI Gym
• Atari
• MuJoCo
• PyBullet

For the details of DRL algorithms, please check out the educational webpage OpenAI Spinning Up.

If you want to learn more about deep reinforcemnet learning, please read the deep-rl-class and run the examples.

• Classic Control
• Atari Benchmark
• Box2d Benchmark
• Mujuco Benchmark
• Petting Zoo

git clone https://github.com/jianzhnie/deep-rl-toolkit.git

# Run the DQN algorithm on the CartPole-v0 environment
python examples/cleanrl/cleanrl_runner.py --env CartPole-v0 --algo dqn
python examples/cleanrl/cleanrl_runner.py --env CartPole-v0 --algo ddqn
python examples/cleanrl/cleanrl_runner.py --env CartPole-v0 --algo dueling_dqn
python examples/cleanrl/cleanrl_runner.py --env CartPole-v0 --algo dueling_ddqn

# Run the C51 algorithm on the CartPole-v0 environment
python examples/cleanrl/cleanrl_runner.py --env CartPole-v0 --algo c51

# Run the DDPG algorithm on the Pendulum-v1 environment
python examples/cleanrl/cleanrl_runner.py --env Pendulum-v0 --algo ddpg

# Run the PPO algorithm on the CartPole-v0 environment
python examples/cleanrl/cleanrl_runner.py --env CartPole-v0 --algo ppo

1. Deep Q-Network (DQN) _{^{(V. Mnih et al. 2015)}}
2. Double DQN (DDQN) _{^{(H. Van Hasselt et al. 2015)}}
3. Advantage Actor Critic (A2C)
4. Vanilla Policy Gradient (VPG)
5. Natural Policy Gradient (NPG) _{^{(S. Kakade et al. 2002)}}
6. Trust Region Policy Optimization (TRPO) _{^{(J. Schulman et al. 2015)}}
7. Proximal Policy Optimization (PPO) _{^{(J. Schulman et al. 2017)}}
8. Deep Deterministic Policy Gradient (DDPG) _{^{(T. Lillicrap et al. 2015)}}
9. Twin Delayed DDPG (TD3) _{^{(S. Fujimoto et al. 2018)}}
10. Soft Actor-Critic (SAC) _{^{(T. Haarnoja et al. 2018)}}
11. SAC with automatic entropy adjustment (SAC-AEA) _{^{(T. Haarnoja et al. 2018)}}

• rllib

  • https://github.com/ray-project/ray
  • https://docs.ray.io/en/latest/rllib/index.html

• coach

  • https://github.com/IntelLabs/coach
  • https://intellabs.github.io/coach

• Pearl

  • https://github.com/facebookresearch/Pearl
  • https://pearlagent.github.io/

• tianshou

  • https://github.com/thu-ml/tianshou
  • https://tianshou.org/en/stable/

• stable-baselines3

  • https://github.com/DLR-RM/stable-baselines3
  • https://stable-baselines3.readthedocs.io/en/master/

• PARL

  • https://github.com/PaddlePaddle/PARL
  • https://parl.readthedocs.io/zh-cn/latest/

• openrl

  • https://github.com/OpenRL-Lab/openrl/
  • https://openrl-docs.readthedocs.io/zh/latest/

• cleanrl

  • https://github.com/vwxyzjn/cleanrl
  • https://docs.cleanrl.dev/