Lizhi-sjtu/DRL-code-pytorch resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1486 stars · 208 forks observed · 2026-08-28

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

A collection of concise PyTorch implementations of deep reinforcement learning algorithms including REINFORCE, A2C, Rainbow DQN, PPO, DDPG, TD3, and SAC. It serves as a clean, readable reference for learning and studying DRL algorithm implementations.

Use cases

• learn how PPO is implemented in pytorch
• study DQN and Rainbow DQN code
• reference implementation of SAC or TD3
• understand discrete vs continuous PPO
• example code for a reinforcement learning course
• compare DRL algorithm implementations side by side

When to choose

• you want readable, minimal implementations of classic DRL algorithms to learn from
• you need a starting point to modify or experiment with RL algorithms in PyTorch
• you are teaching or studying reinforcement learning and want reference code

When to avoid

• you need a production-grade, feature-complete RL training framework with parallel environments
• you need maintained, up-to-date code compatible with current PyTorch and Gym versions
• you need benchmarking infrastructure or hyperparameter tuning tooling

Facets

learning-resource · maturity maintenance

machine-learning reinforcement-learning reinforcement-learning machine-learning tutorials python pytorch drl ppo dqn sac ddpg td3 a2c reinforce educational

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markdown · JSON · MCP: product_card(name="Lizhi-sjtu/DRL-code-pytorch")