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Chapter 17: Reinforcement Learning

Learning Objectives​

By the end of this chapter, you will:

  • Understand the reinforcement learning paradigm
  • Learn about rewards and policies
  • Understand Q-learning
  • Learn about policy gradient methods

Introduction​

Reinforcement learning allows robots to learn through trial and error, discovering optimal behaviors.

Placeholder Content​

Topics to be covered:

  • Markov decision processes
  • Value functions and Q-learning
  • Policy gradient methods
  • Deep reinforcement learning

Did You Know?​

Interesting RL fact.

Real-World Examples​

  1. Game Playing - AlphaGo, Chess engines
  2. Robot Manipulation - Learning pick and place
  3. Autonomous Driving - Learning driving policies

Key Takeaways​

  • To be added

Try It!​

Simple RL environment simulator.

Exercises​

  1. Question
  2. Question
  3. Question

Further Reading​

  • Chapter 16: Deep Learning for Vision
  • Chapter 18: Autonomous Agents

Next Chapter: Chapter 18: Autonomous Agents