Reinforcement Learning

Foundations of Sequential Decision-Making

Module Recap

Congratulations! You have completed the Reinforcement Learning (RL) course, covering the theoretical, mathematical, and practical foundations of how agents learn to make decisions through interaction with an environment. This recap consolidates the key concepts, algorithms, and applications you have learned.

Core Concepts of RL

  • Learned what RL is and how it differs from supervised and unsupervised learning.
  • Explored the four foundational elements: Agent, Environment, Reward, and Policy.
  • Distinguished episodic vs continuing tasks and how these influence returns.
  • Understood the Markov property and its importance in modeling environments.
  • Learned to formalize RL problems as Markov Decision Processes (MDPs).
  • Studied the exploration vs exploitation trade-off and its impact on learning.
Key takeaway: RL enables agents to learn optimal behavior by interacting with environments and adapting strategies based on feedback.

Mathematical FoundationsL

  • Defined state-value (V(s)) and action-value (Q(s,a)) functions.
  • Derived Bellman Expectation and Bellman Optimality equations.
  • Learned to define and reason about optimal policies.
  • Gained intuition for the contraction property that guarantees convergence in iterative algorithms.
Key takeaway: Understanding value functions and Bellman equations is essential for computing and reasoning about optimal policies.

Dynamic Programming (Model-Based RL)

  • Implemented iterative policy evaluation and policy improvement.
  • Combined these into Policy Iteration and Value Iteration.
  • Learned convergence intuition and visualized value function evolution in a Grid World.
Key takeaway: With knowledge of environment dynamics, DP algorithms systematically compute optimal policies and their associated value functions.

Project 1: A Grid World from Scratch

  • Built a simple grid environment manually.
  • Implemented Policy Iteration and Value Iteration.
  • Visualized value functions and optimal policies.
Focus: Hands-on experience with model-based RL and convergence intuition.

Learning Outcomes

By completing this course, students can:

  • Understand RL conceptually and mathematically
  • Derive and implement Bellman equations
  • Apply Dynamic Programming algorithms