Rafael1s/Deep-Reinforcement-Learning-Algorithms resource

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1031 stars · 231 forks observed · 2026-08-28

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

A collection of 32 Jupyter Notebook projects implementing deep reinforcement learning algorithms such as Q-learning, DQN, PPO, DDPG, TD3, SAC, and A2C across many environments like CartPole, LunarLander, and BipedalWalker. Each project includes a detailed training log, making it a reference and educational resource for studying RL methods.

Use cases

• learn deep reinforcement learning algorithms with worked examples
• see training logs for DQN on CartPole or LunarLander
• compare PPO vs DDPG vs TD3 vs SAC implementations
• study policy-gradient and actor-critic methods in notebooks
• find reference implementations for Udacity DRL nanodegree projects
• understand how to solve continuous control environments like BipedalWalker

When to choose

• you want readable notebook-style implementations of classic deep RL algorithms
• you are studying RL and want to see full training logs and results
• you need reference code for Udacity DRL nanodegree environments

When to avoid

• you need a production-ready or maintained RL library with an API
• you want a single unified framework rather than 32 separate notebooks
• you need actively updated code or official license terms

Facets

learning-resource · maturity maintenance

reinforcement-learning machine-learning data-science reinforcement-learning machine-learning tutorials education python jvm deep-reinforcement-learning jupyter-notebooks dqn ppo ddpg td3 sac a2c q-learning udacity training-logs pybullet cartpole lunarlander

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