3 credits.

Prerequisites: D or better in CSCE 580 or graduate standing.

Theory and practical implementation of reinforcement learning methods with deep neural networks. Markov decision processes, policy iteration, value iteration, q-learning, SARSA, deep neural networks, approximate value iteration, deep q-networks, policy gradients, model-based reinforcement learning, multi-armed bandits, partial observability.