Exact methods to solve Markov Decision Processes (MDPs) are ineffective in practice either because there are a large number of states or because the model of the MDP is not known. Approximate Dynamic Programming (ADP) algorithms are approximate solution methods for MDPs with large number of states. Reinforcement Learning (RL) algorithms are sample trajectory based solution methods for MDPs. The talk focuses on conditions that guarantee performance of ADP algorithms and stability of RL algorithms.
Announcements from Dept.
Mar 18, 2017 : Comprehensive Examination - Revised Registration Forms
Jan 10, 2017 : IITM Summer Fellowship Programme.
Dec 16, 2016 : The Semi-annual Progress Report for MS/PhD Scholars is due by Dec.