rlcode/reinforcement-learning resource

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3659 stars · 735 forks observed · 2026-08-28

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

A collection of minimal, clean, one-file-per-algorithm reinforcement learning examples in Python, covering classic methods like Q-Learning and SARSA up to deep RL like DQN, PPO, and A3C. It includes benchmark results on Grid World, CartPole, and Atari environments with reproducible training reports.

Use cases

• learn reinforcement learning from readable code examples
• understand how DQN works with a minimal implementation
• study PPO and actor-critic algorithms in one file
• find reference implementations of Q-learning and SARSA
• compare RL algorithm benchmarks on Atari and CartPole
• get started with deep reinforcement learning in Python

When to choose

• you want clean, minimal, educational RL implementations rather than a heavy framework
• you are learning RL algorithms and prefer one readable file per algorithm
• you want reproducible benchmark baselines for DQN and PPO on standard environments

When to avoid

• you need a production-ready RL library with parallel environments and hyperparameter tuning
• you want a framework API to plug algorithms into your own project rather than standalone examples
• you need state-of-the-art performance or extensive multi-seed evaluation

Facets

learning-resource · maturity active

reinforcement-learning machine-learning deep-learning benchmarking reinforcement-learning machine-learning education tutorials python cross-platform dqn ppo actor-critic policy-gradient q-learning sarsa atari cartpole grid-world pytorch educational-examples gpu

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