qfettes/DeepRL-Tutorials resource

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1078 stars · 326 forks observed · 2026-08-28

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

A collection of IPython/Jupyter notebooks implementing deep reinforcement learning algorithms in PyTorch, covering DQN variants (Double, Dueling, Noisy Nets, Prioritized Replay, Categorical, Rainbow, Quantile Regression, DRQN) and actor-critic methods (A2C, GAE). Each notebook maps to a published paper and prioritizes readability for learning.

Use cases

• learn deep reinforcement learning algorithms from readable PyTorch implementations
• understand the Rainbow DQN paper by reading annotated code
• study DQN variants like double DQN, dueling DQN, and noisy networks
• find a reference implementation of prioritized experience replay
• learn actor-critic methods like A2C and PPO with GAE
• supplement RL paper reading with working notebook code

When to choose

• you want readable, educational implementations tied to specific RL papers
• you prefer PyTorch and Jupyter notebooks for studying algorithms
• you are learning distributional RL, Rainbow, or quantile regression DQN

When to avoid

• you need production-ready, efficient, or maintained RL libraries
• you need a library with a stable API, tests, or license
• you want on-policy algorithms beyond A2C/GAE or the latest RL methods

Facets

learning-resource · maturity maintenance

machine-learning reinforcement-learning developer-tools reinforcement-learning machine-learning tutorials deep-learning python cross-platform pytorch jupyter-notebooks dqn rainbow ppo a2c actor-critic prioritized-experience-replay educational gpu

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