Reinforcement Learning Introduction
Coming Soon
This lesson is currently under development. Check back soon for comprehensive content covering:
- MDP Fundamentals: States, actions, rewards, policies, value functions
- Q-Learning: Temporal difference learning, Q-tables, exploration vs. exploitation
- Policy Gradients: REINFORCE, actor-critic methods, PPO (Proximal Policy Optimization)
- Reward Shaping: Designing rewards for robot tasks, avoiding reward hacking
- Sim-to-Real Transfer: Domain randomization, reality gap, training in simulation
Expected Completion: This lesson will be available soon.
Learning Objectives
By the end of this lesson, you will be able to:
- Formulate robot tasks as MDPs with states, actions, and rewards
- Implement Q-learning for simple robot control tasks
- Understand policy gradient methods (PPO, SAC) for continuous control
- Design reward functions that encourage desired robot behaviors
- Apply sim-to-real techniques to transfer learned policies to hardware
Further Reading
- Reinforcement Learning: An Introduction by Sutton & Barto
- Spinning Up in Deep RL by OpenAI — Hands-on RL education
- Sim-to-Real Transfer — Domain randomization paper
What's Next?
You've completed Part 1: Foundations! Continue to Part 2: ROS 2 Ecosystem to begin hands-on robot programming.
This lesson completes Chapter 2: AI Fundamentals Review and Part 1: Foundations of Physical AI