Reinforcement Learning

Catalog description

This course delves into advanced reinforcement learning techniques, focusing on theoretical foundations and practical applications. Topics include policy iteration, actor-critic methods, deep reinforcement learning, and multi-agent sys- tems. Students will develop reinforcement learning models using Py - thon libraries such as TensorFlow or PyTorch, with applications in ar - eas like robotics, autonomous systems, and AI-driven decision-making processes.