Reinforcement Learning Prog

Course description

Reinforcement learning utilizes software agents to make decisions in a simulated environment and use a reward/penalty system for achieving goals or making mistakes. Through numerous iterations of the simulation the algorithms learn and adjust in order to provide the best possible outcome. Students learn how to leverage reinforcement learning concepts such as dynamic programming, Q-learning, State-Action-Reward-State-Action (SARSA) and Deep Deterministic Policy Gradient (DDPG) to solve artificial intelligence problems that are highly dimensional.

Students registering for credit courses for the first time must declare a program at the point of registration. Declaring a program does not necessarily mean students must complete a program, individual courses may be taken for skill improvement and upgrading.

For more information, please contact Continuing Education