Approximate Dynamic Programming

Dynamic programming provides a framework for modeling problems of

sequential decision under uncertainty and algorithms for computing

optimal decision strategies. Due to the curse of dimensionality, the

associated computational requirements become prohibitive in many

practical contexts. Approximate dynamic programming algorithms aim

to approximate optimal decision strategies using limited

computational resources. This talk will provide an introduction and

cover case studies involving Tetris, scheduling of queues, and

network revenue management.