vmayoral/basic_reinforcement_learning resource

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1224 stars · 368 forks observed · 2026-08-28

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

An introductory tutorial series on reinforcement learning with step-by-step Jupyter Notebook walkthroughs covering Q-learning, SARSA, DQN, DDPG, and policy gradient methods. Includes exercises with OpenAI Gym and robotics simulation using ROS and Gazebo.

Use cases

• learn reinforcement learning from scratch
• understand q-learning with code examples
• implement deep q-networks tutorial
• get started with openai gym
• learn ddpg and policy gradient methods
• apply reinforcement learning to robotics with ros and gazebo
• find beginner reinforcement learning tutorials

When to choose

• you are new to reinforcement learning and want guided, incremental tutorials
• you prefer learning by coding algorithms from scratch in notebooks
• you want coverage from tabular methods (Q-learning, SARSA) through deep RL (DQN, DDPG, policy gradients)
• you want to combine RL with robotics simulation via ROS and Gazebo

When to avoid

• you need a production-ready RL library or framework rather than educational code
• you need state-of-the-art algorithms like PPO, SAC, or model-based RL
• you need actively maintained content - several tutorials are unfinished or abandoned
• you need scalable, performant training infrastructure

Facets

learning-resource · maturity maintenance

reinforcement-learning machine-learning deep-learning simulation benchmarking reinforcement-learning machine-learning artificial-intelligence tutorials robotics python cross-platform q-learning sarsa dqn ddpg policy-gradient openai-gym jupyter-notebooks ros gazebo step-by-step-tutorials

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