vietnh1009/Super-mario-bros-A3C-pytorch resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1112 stars · 237 forks observed · 2026-08-28

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

A clean PyTorch implementation of the Asynchronous Advantage Actor-Critic (A3C) algorithm that trains an agent to play Super Mario Bros via OpenAI Gym. It is written as a simplified, readable reference implementation that follows the original DeepMind paper closely.

Use cases

• learn how the A3C reinforcement learning algorithm works
• train an AI agent to play Super Mario Bros
• study a minimal PyTorch actor-critic implementation
• reproduce results from the Asynchronous Methods for Deep Reinforcement Learning paper
• get a starting codebase for my own RL game experiments
• compare a clean A3C implementation against more complicated repos

When to choose

• you want a simple, readable A3C reference implementation in PyTorch
• you are learning reinforcement learning and want minimal setup code
• you want to train an agent on a classic NES game environment

When to avoid

• you need a production-grade or actively maintained RL framework
• you need distributed or highly optimized training at scale
• you want support for many environments beyond Super Mario Bros out of the box

Facets

learning-resource · maturity maintenance

reinforcement-learning deep-learning machine-learning reinforcement-learning artificial-intelligence python cross-platform a3c pytorch gym super-mario-bros actor-critic educational game-development

Member repositories

For agents

markdown · JSON · MCP: product_card(name="vietnh1009/Super-mario-bros-A3C-pytorch")