Game Theoretic Learning and Spectrum Management - Part 2
Dive into the second part of a three-part lecture series on game theoretic learning and spectrum management presented by Amir Leshem and Kobi Cohen for the IEEE Signal Processing Society. Explore key concepts such as single-player multi-arm bandit problems, stochastic map formulation, and sublinear regret. Examine various algorithms including UCB1, epsilon-greedy, and adaptive sequential algorithms. Investigate Markovian rewards, restless MAPs, and regret minimization techniques. Learn about exploration and exploitation network structures, reinforcement learning, and deep reinforcement learning applications. Gain insights into single-agent learning and exploration phases through simulations and practical examples in this comprehensive one-hour lecture.