NDLI: Leader-Follower semi-Markov Decision Problems: Theoretical Framework and Approximate Solution

Content Provider	IEEE Xplore Digital Library
Author	Tharakunnel, K. Bhattacharyya, S.
Copyright Year	2007
Description	Author affiliation: Dept. of Inf. & Decision Sci., Illinois Univ., Chicago, IL (Tharakunnel, K.; Bhattacharyya, S.)
Abstract	Leader-follower problems are hierarchical decision problems in which a leader uses incentives to induce certain desired behavior among a set of self-interested followers. Dynamic leader-follower problems extend this structure to multi-period decision situations. In this work we propose a Markov decision process (MDP) framework for a class of dynamic leader-follower problems that have important applications and discuss their approximate solution using reinforcement learning (RL). In these problems, the leader makes incentive decisions intermittently while the followers make their decisions in every period. Our theoretical framework and computational approach are based on the observation that such dynamic problems can be thought of as consisting of two coupled sequential decision processes, that of the leader and of the followers. In our formulation, the leader's decision problem that has the structure of a single-agent semi-Markov decision process (SMDP), and the followers' sequential decision problem structured as a stochastic game (multiagent competitive MDP) operate over the same state space. We call this MDP framework a leader-follower semi-Markov decision process (LFSMDP). We consider approximate solution of these problems using RL and demonstrate the solution approach in the special case where the followers' stochastic game is a repeated game.
Starting Page	111
Ending Page	118
File Size	9920971
Page Count	8
File Format	PDF
ISBN	1424407060
DOI	10.1109/ADPRL.2007.368177
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning Peer to peer computing Decision making Stochastic processes Pricing Electricity supply industry Dynamic programming State-space methods Communication networks Game theory
Content Type	Text
Resource Type	Article

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