Online Adaptations

Lectures and Office hours will be live streamed on Zoom.

 

Extended Description

This course is about learning to control the movement of humans, animals, and robots, with application to character animation, computer vision, robotics, and biological motor control. The bulk of the course will focus on reinforcement learning (RL), which has seen many advances over the past five years. Topics to be covered include motion control problems, sequential decision problems, RL fundamentals, dynamic programming, tabular methods, deep Q-learning, policy gradient methods, common policy gradient algorithms (A2C, A3C, TRPO, PPO), common Q-learning algorithms (DDPG, SAC, TD3), model-based RL and model-predictive control, imitation learning, RL and representation learning, sim-to-real, RL frameworks, forward/inverse kinematics and dynamics, linear quadratic regulators, and advanced topics in RL.

Course Info
Section
101
Term
Term 1
Session
2021W
Dates
Days
Mon Wed
Time (start)
3:00 PM
Time (end)
4:30 PM
Date (start)
Date (end)
Location
Building
ICCS
Room
246