Week 1: Markov decision process, finite horizon problem formulation, examples, principle of optimality, Bellman equation
Week 2: Infinite horizon problems, Optimality criteria (average cost, discounted cost), Bellman equation, optimality of Markov policies
Week 3: Computing optimal policies, linear programming formulation
Week 4: Partially observed Markov decision processes, reduction to the information state
Week 5: LQR problem, Kalman filter
Week 6: LQG problem, separation principle, optimality of linear policies
Week 7: Witsenhausen counterexample. information structure,
Week 8: Intrinsic model of stochastic control, LQG static teams, optimality of linear policies
Week 9: Variants of the Witsenhausen problem, Bansal Basar problem, optimizer’s approach
Week 10: Communication and decentralized control. Canonical communication problems of source coding, channel coding and rate distortion theory
Week 11: Shannon’s coding theorems
Week 12: Shannon’s coding theorems and optimizer’s approach
DOWNLOAD APP
FOLLOW US