quantumiracle/Popular-RL-Algorithms resource

Health v2 · maintenance only

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Adoption not part of the score

1358 stars · 147 forks observed · 2026-08-28

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

A personal collection of PyTorch implementations of popular model-free reinforcement learning algorithms (SAC, TD3, PPO, DDPG, Q-learning, QMIX, and more) tested on OpenAI Gym and a custom Reacher environment. It is a study/research reference rather than a packaged library, with multiple implementation variants shown for comparison.

Use cases

• learn how SAC is implemented in PyTorch
• compare multiple implementations of the same RL algorithm
• study PPO or TD3 source code for a course
• find reference code for model-free RL algorithms
• get a starting point for implementing a custom RL algorithm
• understand differences between SAC versions

When to choose

• you want readable, educational implementations of classic RL algorithms
• you want to see multiple variants of an algorithm side by side
• you are studying reinforcement learning and want reference code rather than a black-box library

When to avoid

• you need a production-ready or well-structured RL library
• you want a stable high-level API for training RL agents
• you need maintained, tested code with clean abstractions

Facets

learning-resource · maturity maintenance

machine-learning reinforcement-learning reinforcement-learning machine-learning tutorials python pytorch model-free-rl openai-gym sac td3 ppo research-code jupyter-notebook

Member repositories

For agents

markdown · JSON · MCP: product_card(name="quantumiracle/Popular-RL-Algorithms")