Reinforcement learning is a subfield of artificial intelligence which deals with learning from repeated interactions with an environment. Reinforcement learning is the basis for algorithms for playing strategy games such as Chess, Go, Backgammon, and Starcraft, as well as a number of algorithms throughout robotics, operations research, and other fields of engineering. This course will cover the fundamental algorithms of reinforcement learning, focusing on the core principles underlying these methods. Topics covered will include Dynamic Programming, Markov Decision Processes, Value Iteration, Policy Iteration, Temporal Difference Methods and Monte Carlo, Function Approximation in Reinforcement Learning and Neural Networks