sweetice/Deep-reinforcement-learning-with-pytorch resource

Health v2 · maintenance only

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4651 stars · 895 forks observed · 2026-08-28

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

A collection of clear PyTorch implementations of classic and state-of-the-art deep reinforcement learning algorithms such as DQN, A3C, PPO, SAC, TD3, and TRPO. It is designed as an educational resource for people learning deep RL algorithms.

Use cases

• learn deep reinforcement learning algorithms with pytorch
• study dqn implementation on cartpole
• understand ppo and sac algorithm code
• reference implementations of actor-critic methods
• compare policy gradient algorithms side by side
• run td3 on bipedalwalker

When to choose

• you want readable educational implementations of RL algorithms
• you are learning deep RL and want to read clean pytorch code
• you need reference code for DQN, PPO, SAC, TD3, or related algorithms

When to avoid

• you need a production-ready or well-maintained RL framework
• you require modern Python versions or up-to-date dependencies
• you need a stable API for training RL agents at scale

Facets

learning-resource · maturity maintenance

reinforcement-learning machine-learning deep-learning reinforcement-learning machine-learning deep-learning tutorials python cross-platform pytorch dqn ppo actor-critic sac td3 a3c ddpg trpo policy-gradient educational

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markdown · JSON · MCP: product_card(name="sweetice/Deep-reinforcement-learning-with-pytorch")