Deep Reinforcement Learning - Tutorial Session: Review of Q-Learning

Explore fundamental concepts in deep reinforcement learning through this 51-minute tutorial session that provides a comprehensive review of Q-Learning algorithms and their applications. Begin with a thorough examination of Markov Decision Processes (MDP) as the mathematical foundation for reinforcement learning problems. Delve into exact MDP solutions before progressing to parametric Q-Learning approaches that enable handling of complex, high-dimensional state spaces. Master practical implementation details essential for successful deployment of Q-Learning algorithms in real-world scenarios. Conclude with detailed walkthroughs of both Q-Learning and Actor-Critic algorithms, comparing their methodologies and understanding when to apply each approach. Led by Teaching Assistant Anikait Singh, this session serves as an essential foundation for understanding modern deep reinforcement learning techniques and prepares you for advanced topics in the field.