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1
Introduction
2
High Level Plan
3
Structure
4
Markov Transitions
5
Questions
6
Markov Control Process
7
Control Objective
8
Randomizing Policies
9
Markov Property
10
Powerful observable mdps
11
Basic methods
Description:
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

Simons Institute
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