12 Reinforcement learning
This chapter covers
- Grasping the fundamental principles underlying reinforcement learning
- Understanding the Markov decision process
- Comprehending the actor-critic architecture and proximal policy optimization
- Getting familiar with noncontextual and contextual multi-armed bandits
- Applying reinforcement learning to solve optimization problems
Reinforcement learning (RL) is a powerful machine learning approach that enables intelligent agents to learn optimal or near-optimal behavior through interacting with their environments. This chapter dives into the key concepts and techniques within RL, shedding light on its underlying principles as essential background knowledge. Following this theoretical exposition, the chapter ...
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