ikostrikov/pytorch-a3c

Health v2 · maintenance only

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Adoption not part of the score

1334 stars · 281 forks observed · 2026-08-28

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

A PyTorch implementation of the Asynchronous Advantage Actor Critic (A3C) reinforcement learning algorithm from the DeepMind paper 'Asynchronous Methods for Deep Reinforcement Learning'. It trains agents on Atari environments using multiple asynchronous worker processes with a shared optimizer.

Use cases

• train an A3C agent on Atari games like Pong and Breakout
• reproduce results from the asynchronous deep RL paper
• learn how asynchronous actor-critic is implemented in PyTorch
• experiment with parallel reinforcement learning workers
• compare A3C against A2C/PPO baselines
• use A3C code as a starting point for RL research

When to choose

• you specifically need the A3C algorithm
• you want a small, readable reference implementation in PyTorch
• you are studying asynchronous RL methods for research or education

When to avoid

• you want the best-performing RL algorithm - the author recommends A2C, PPO, or ACKTR instead
• you need a maintained, feature-rich RL framework
• you need Python 2 support or recent updates - the repo is Python 3 only and largely inactive

Facets

library · maturity maintenance

machine-learning reinforcement-learning reinforcement-learning machine-learning deep-learning python a3c actor-critic pytorch asynchronous-methods research-code atari linux macos

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