udacity/deep-reinforcement-learning resource

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5176 stars · 2373 forks observed · 2026-08-28

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

The official repository of Jupyter Notebook tutorials and exercises for Udacity's Deep Reinforcement Learning Nanodegree program. It provides PyTorch-based implementations of classic and deep RL algorithms such as dynamic programming, DQN, PPO, and DDPG, mostly using OpenAI Gym environments.

Use cases

• learn reinforcement learning from scratch with hands-on notebooks
• implement DQN to solve an OpenAI Gym environment
• study policy gradient methods like REINFORCE and PPO
• understand temporal-difference learning such as Q-Learning and Sarsa
• practice discretizing continuous state spaces with tile coding
• find reference solutions for OpenAI Gym RL exercises

When to choose

• you want structured, tutorial-style notebooks for learning deep RL
• you prefer PyTorch implementations of classic RL algorithms
• you are following or supplementing Udacity's Deep RL Nanodegree

When to avoid

• you need a production-ready RL library or framework
• you want state-of-the-art algorithms beyond the course scope
• you need actively maintained code compatible with modern PyTorch versions

Facets

learning-resource · maturity maintenance

reinforcement-learning machine-learning deep-learning reinforcement-learning machine-learning tutorials education python cross-platform pytorch openai-gym dqn ppo ddpg nanodegree jupyter-notebooks udacity

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