sdpkjc/abcdrl repository overview

⁠abcdRL (Implement a RL algorithm in four simple steps)

English | 简体中文⁠

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.

Documentation ➡️ docs.abcdrl.xyz⁠

Roadmap🗺️ #57⁠

⁠🚀 Quickstart

Open the project in Gitpod🌐 and start coding immediately.

Using Docker📦:

# 0. Prerequisites: Docker & Nvidia Drive & NVIDIA Container Toolkit
# 1. Run DQN algorithm
docker run --rm --gpus all sdpkjc/abcdrl python abcdrl/dqn_torch.py

For detailed installation instructions 👀⁠

⁠🐼 Features

• 👨‍👩‍👧‍👦 Unified code structure
• 📄 Single-file implementation
• 🐷 Low code reuse
• 📐 Minimizing code differences
• 📈 Tensorboard & Wandb integration
• 🛤 PEP8(code style) & PEP526(type hint) compliant

⁠🗽 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_torch.py, dqn_tf.py, dqn_atari_torch.py, dqn_atari_tf.py}
• Deep Deterministic Policy Gradient (DDPG)⁠ _{ddpg_torch.py}
• Twin Delayed Deep Deterministic Policy Gradient (TD3)⁠ _{td3_torch.py}
• Soft Actor-Critic (SAC)⁠ _{sac_torch.py}
• Proximal Policy Optimization (PPO)⁠ _{ppo_torch.py}

• Double Deep Q Network (DDQN)⁠ _{ddqn_torch.py, ddqn_tf.py}
• Prioritized Deep Q Network (PDQN)⁠ _{pdqn_torch.py, pdqn_tf.py}

⁠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}
}