Reinforcement Learning

1 Unit · Graduate

The first half of the course will cover classical reinforcement learning concepts: MDPs, tree search, trajectory optimization, value iteration, policy iteration, and SARSA. The second half will cover modern deep reinforcement learning: deep Q learning, policy gradients, Monte Carlo tree search, inverse reinforcement learning, noise contrastive estimation. We will conclude by discussing philosophical questions related to AI and superhuman intelligence.