Deep Reinforcement Learning

Catalog description

This course covers the theory and approaches to building autonomous agents that learn to act and accomplish goals in dynamic environments. Topics include Markov decision processes (MDPs), learning behavioral policies, policy gradients, policy iteration, value iteration, agent explorations vs. exploitation, and Q learning.