Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 2 - Given a Model of the World

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Professor Emma Brunskill, Stanford University https://stanford.io/3eJW8yT Professor Emma Brunskill Assistant Professor, Computer Science Stanford AI for Human Impact Lab Stanford Artificial Intelligence Lab Statistical Machine Learning Group To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs234/i... 0:00 Introduction 2:55 Full Observability: Markov Decision Process (MDP) 3:55 Recall: Markov Property 4:50 Markov Processor Markov Chain 5:53 Example: Mars Rover Markov Chain Transition Matrix, P 12:06 Example: Mars Rover Markov Chain Episodes 13:05 Markov Reward Process (MRP) 14:37 Return & Value Function 16:32 Discount Factor 18:23 Example: Mars Rover MRP 23:19 Matrix Form of Bellman Equation for MRP 26:52 Iterative Algorithm for Computing Value of a MRP 33:29 MDP Policy Evaluation, Iterative Algorithm 34:44 Policy Evaluation: Example & Check Your Understanding 36:39 Practice: MDP 1 Iteration of Policy Evaluation, Mars Rover Example 50:48 MDP Policy Iteration (PI) 55:44 Delving Deeper into Policy Improvement Step