Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
409206 | Neurocomputing | 2014 | 8 Pages |
Abstract
We consider state-dependent pricing in a two-player service market stochastic game where state of the game and its transition dynamics are modeled using a semi-Markovian queue. We propose a multi-time scale actor–critic based reinforcement algorithm for multi-agent learning under self-play and provide experimental results on Nash convergence.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
D. Krishna Sundar, K. Ravikumar,