ikostrikov/pytorch-a2c-ppo-acktr-gail

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

3903 stars · 840 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A PyTorch implementation of several deep reinforcement learning algorithms: A2C, PPO, ACKTR, and GAIL (imitation learning). It works with OpenAI Gym environments including Atari, MuJoCo, PyBullet, and DeepMind Control Suite.

Use cases

• train an RL agent with PPO in PyTorch
• implement A2C for Atari games
• run reinforcement learning on MuJoCo continuous control tasks
• try imitation learning with GAIL
• compare policy gradient algorithms A2C PPO ACKTR
• learn deep reinforcement learning from a reference implementation
• train RL agents on PyBullet robotics environments

When to choose

• you want well-tuned, citable PyTorch implementations of A2C, PPO, ACKTR, or GAIL
• you need a single codebase that works across Atari and continuous-control Gym environments
• you are doing RL research or coursework and want a readable reference implementation

When to avoid

• you need Soft Actor-Critic or the latest RL algorithms - the author recommends his newer JAX repository
• you want a maintained production RL framework with active development
• you need distributed or massively scalable RL training

Facets

library · maturity maintenance

machine-learning reinforcement-learning deep-learning reinforcement-learning machine-learning deep-learning robotics gaming-tools python a2c ppo acktr gail pytorch openai-gym atari mujoco pybullet imitation-learning policy-gradient linux macos gpu

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markdown · JSON · MCP: product_card(name="ikostrikov/pytorch-a2c-ppo-acktr-gail")