abcdrl 0.2.0a4

Description:

abcdrl 0.2.0a4

abcdRL (Implement a RL algorithm in four simple steps)
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abcdRL is a Modular Single-file Reinforcement Learning Algorithms Library that provides modular design without strict and clean single-file implementation.

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

abcdRL mainly references the single-file design philosophy of vwxyzjn/cleanrl and the module design of PaddlePaddle/PARL.

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.py

For detailed installation instructions 👀
🐼 Features

👨‍👩‍👧‍👦 Unified code structure
📄 Single-file implementation
🐷 Low code reuse
📐 Minimizing code differences
📈 Tensorboard & Wandb support
🛤 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)
Deep Deterministic Policy Gradient (DDPG)
Twin Delayed Deep Deterministic Policy Gradient (TD3)
Soft Actor-Critic (SAC)
Proximal Policy Optimization (PPO)



Double Deep Q Network (DDQN)
Prioritized Deep Q Network (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}
}