Reinforcement Learning Prog

Reinforcement Learning Prog

AIDI 1008

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.

Credits

3

Course Hours

42

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.

Contact coned@georgiancollege.ca or call 705.722.5149 for more information.