CS 294-7, Spring 2003: Reinforcement Learning
Reading list




Books


Week 1 (1/22): Agents, environments, Markov decision processes

Week 2 (1/29): Dynamic programming

Week 3 (2/5): Dynamic programming contd.

Week 4 (2/12): Monte Carlo methods

Week 5 (2/19): Samuel's checker player

Week 6 (2/26): Running averages, temporal difference learning

Week 7 (3/5): Q-decomposition, convergence of Q-learning

Week 8 (3/12): Shaping, lambda, function approximation

Week 9 (3/19): Function approximation contd.: update, examples, convergence

Week 10 (3/26): SPRING BREAK

Week 11 (4/2): Partially observable MDPs

Project proposals due.

Week 12 (4/9): Policy search methods

Week 13 (4/16): Multiagent reinforcement learning

Week 14 (4/23): Multiagent reinforcement learning contd.; hierarchical reinforcement learning

Week 15 (4/30): Hierarchical reinforcement learning

Week 16 (5/7): Exploration; evolution


Week 17 (5/14): Project presentations