abcdRL (Implement a RL algorithm in four simple steps)

abcdRL is a Modular Single-file Reinforcement Learning Algorithms Library that provides modular design without strict and clean single-file implementation.

Understand the full implementation details of the algorithm in a single file quickly when reading the code; Benefit from a lightweight modular design, only need to focus on a small number of modules when modifying the algorithm.

Roadmap🗺️ #57

🗽 Design Philosophy

• "Copy📋", not "Inheritance🧬"
• "Single-file📜", not "Multi-file📚"
• "Features reuse🛠", not "Algorithms reuse🖨"
• "Unified logic🤖", not "Unified interface🔌"

✅ Implemented Algorithms

Weights & Biases Benchmark Report ➡️ report.abcdrl.xyz

• Deep Q Network (DQN) _{dqn.py, dqn_tf.py, dqn_atari.py}
• Deep Deterministic Policy Gradient (DDPG) _ddpg
• Twin Delayed Deep Deterministic Policy Gradient (TD3) _td3
• Soft Actor-Critic (SAC) _sac
• Proximal Policy Optimization (PPO) _ppo

• Double Deep Q Network (DDQN) _ddqn
• Prioritized Deep Q Network (PDQN) _pdqn

Citing abcdRL

@misc{zhao_abcdrl_2022,
    author = {Yanxiao, Zhao},
    month = {12},
    title = {{abcdRL: Modular Single-file Reinforcement Learning Algorithms Library}},
    url = {https://github.com/sdpkjc/abcdrl},
    year = {2022}
}