REINFORCEMENT LEARNING.

Catalog description

Pre-requisites: EENG 383 or permission of instructor. Corequisite: EENG 428L . Introduces various reinforcement learning (RL) algorithms such as Dynamic Programming (DP), Monte Carlo (MC) learning, Temporal-Difference (TD) learning, Dyna-Q learning etc. These RL learning algorithms are built up in iterative ways from Bellman equations based on the interactions between agent(s) and environment. Furthermore, exploration, exploitation, and effective search algorithms are introduced in the context of the RL learning process. Companion course to EENG 428L .