coreylynch/async-rl

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1003 stars · 168 forks observed · 2026-09-03

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A TensorFlow + Keras implementation of asynchronous 1-step Q-learning from the DeepMind 'Asynchronous Methods for Deep Reinforcement Learning' (A3C) paper, using OpenAI Gym's Atari environments. It runs multiple actor-learner threads to stabilize training without experience replay, and includes a work-in-progress async advantage actor-critic implementation.

Use cases

• train a deep Q-network on Atari games with async actor-learner threads
• reproduce the asynchronous 1-step Q-learning results from the A3C paper
• learn how to implement deep reinforcement learning with TensorFlow and Keras
• evaluate trained RL agents against OpenAI Gym Atari environments
• run reinforcement learning experiments on low-memory hardware without experience replay
• visualize RL training curves like episode rewards and max Q values in TensorBoard

When to choose

• you want a compact, readable reference implementation of async Q-learning on Atari
• you're studying the A3C paper and want code to follow along with
• you need RL training that fits in a few GB of RAM without experience replay

When to avoid

• you need a maintained library - the last release was 2018 and it targets old TensorFlow/Keras versions
• you want production-grade or modern RL tooling like Stable-Baselines3 or RLlib
• you need A3C/actor-critic fully implemented - that part is marked work-in-progress

Facets

library · maturity abandoned

machine-learning reinforcement-learning deep-learning benchmarking reinforcement-learning machine-learning deep-learning gaming-tools python cross-platform reinforcement-learning q-learning a3c atari openai-gym actor-learner tensorflow keras research-code linux macos

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