Reinforcement Learning, Fall 2018
Rubric: AE 598RL and ME 598MW
Time: 10–11:50am, Tuesday and Thursday
Location: 1022 Lincoln Hall
Description: Theory and practice of reinforcement learning as a tool for machine learning and artificial intelligence, applied to control, dynamics, and robotics, with a particular emphasis on computation. Topics will include reinforcement learning algorithms (temporal difference, Q-learning, policy gradient, actor-critic), function approximation and the use of deep neural networks, and efficient implementation on parallel architectures. Restrictions and prerequisites: CS 446 or equivalent; experience with TensorFlow, PyTorch, or equivalent.