2025-2026 Undergraduate & Graduate Catalog [In Progress]
AI 451 - Reinforcement Learning
This course explores foundational and advanced reinforcement learning techniques, including markov decision processes, dynamic programming, monte carlo methods, and model-free approaches like Q(Quality)-learning. It covers continuous state spaces with state aggregation and delves into topics such as deep Q(Quality)-Learning, Advantage Actor-Critic, etc. applicable to real-world environments. Dual-listed with AI 551. Offered fall and winter semesters. Prerequisites: CIS 378
Credits: 3