Explore the foundations of reinforcement learning in this lecture from the Theory of Reinforcement Learning Boot Camp. Delve into planning and Markov Decision Processes with experts Csaba Szepesvari and Mengdi Wang. Gain insights into high-level planning structures, Markov transitions, control processes, and the Markov property. Examine randomizing policies, powerful observable MDPs, and basic methods in this comprehensive introduction to key reinforcement learning concepts.
Planning and Markov Decision Processes - Part 1
Overview
Syllabus
Introduction
High Level Plan
Structure
Markov Transitions
Questions
Markov Control Process
Control Objective
Randomizing Policies
Markov Property
Powerful observable mdps
Basic methods
Taught by
Simons Institute
