Chapter 12: Reinforcement Learning

Reinforcement Learning (RL) represents a fundamentally different paradigm from supervised and unsupervised learning. Instead of learning from labeled examples or finding patterns in data, RL agents learn by interacting with an environment and receiving feedback in the form of rewards.

The key challenge in RL is the credit assignment problem: when an agent receives a reward, it must determine which of its past actions contributed to that reward. This is complicated by delayed rewards—a good action might not produce a reward until many steps later.

RL has achieved remarkable successes: defeating world champions at Go and Chess, learning to play Atari games from raw pixels, controlling robots, optimizing data center cooling, and training the RLHF component of modern language models like ChatGPT.

The fundamental framework for RL is the Markov Decision Process (MDP), which formalizes the agent-environment interaction loop. Two main approaches exist for solving MDPs: value-based methods (learning which states/actions are valuable) and policy-based methods (directly learning which actions to take).

This chapter covers:

• MDP Framework: The mathematical formalization of sequential decision-making with states, actions, rewards, and transitions
• Q-Learning: A value-based algorithm that learns optimal action-values through temporal difference updates
• Policy Gradients: Methods that directly parameterize and optimize the policy using gradient ascent
• Deep RL: Combining neural networks with RL for high-dimensional state spaces and complex policies