Explore the intricacies of online learning in Markov Decision Processes (MDPs) in this comprehensive lecture from the Theory of Reinforcement Learning Boot Camp. Delve into topics such as adversarial scenarios, performance measures, oblivious and non-oblivious adversaries, and the challenges of learning with changing transitions. Examine the formal protocol for online learning in fixed MDPs, temporal dependencies, and regret decomposition. Discover the MDP-Expert algorithm, its guarantees, and applications in bandit feedback scenarios. Investigate online linear optimization, mirror descent, and the Online Relative Entropy Policy Search (O-REPS) algorithm. Compare various guarantees and explore function approximation in MDP-E. Gain insights into O-REPS with uncertain models and consider future directions in this field of study.
Online Learning in Markov Decision Processes - Part 2
Overview
Syllabus
Intro
MARKOV DECISION PROCESSES
ADVERSARIAL
PERFORMANCE MEASURE: RE
OUTLINE
NON-OBLIVIOUS ADVERSARI
WHAT WENT WRONG?
OBLIVIOUS ADVERSARIES
LEARNING WITH CHANGING TRANSITIONS IS HARD
PROOF CONSTRUCTION
SLOWLY CHANGING MDPS
FORMAL PROTOCOL Online learning in a fixed MDP For each round t = 1,2, ..., • Learner observes state X, EX
TEMPORAL DEPENDENCES
REGRET DECOMPOSITION
THE DRIFT TERMS
LOCAL-TO-GLOBAL
THE MDP-EXPERT ALGORITHE
GUARANTEES FOR MDP-E
BANDIT FEEDBACK
ONLINE LINEAR OPTIMIZATIO
ONLINE MIRROR DESCENT
THE ONLINE REPS ALGORITH O-REPS
GUARANTEES FOR O-REPS
COMPARISON OF GUARANTE
MDP-E WITH FUNCTION APPROXIMATION MDP-E only needs a good approximation of the action-value
O-REPS WITH UNCERTAIN MO
OUTLOOK
Taught by
Simons Institute
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Online Learning in Markov Decision Processes - Part 1
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Online Learning and Bandits - Part 2
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