Prerequisites: Proficiency in Python programming. Familiarity with fundamental machine learning concepts (supervised/unsupervised learning). Basic understanding of linear algebra and probability.

Level: Intermediate

What You'll Learn

• RL Fundamentals

  Understand the core components of RL: agents, environments, states, actions, rewards, policies.

• Markov Decision Processes

  Formulate problems using MDPs and understand their properties.

• Value Functions & Bellman Equations

  Grasp the concepts of state-value and action-value functions and derive Bellman equations.

• Model-Free Learning

  Implement and differentiate between Monte Carlo and Temporal Difference learning methods (Q-learning, SARSA).

• Function Approximation

  Apply function approximation techniques in RL for large state spaces.

• Deep Q-Networks (DQN)

  Understand the basic principles behind DQN and its components like experience replay.

• RL Implementation

  Build and train simple RL agents using Python and relevant libraries.