An actor-critic algorithm for multi-agent learning in queue-based stochastic games
Document Type
Article
Publication Title
Neurocomputing
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.
Publication Date
1-4-2014
Publisher
Elsevier
Volume
Vol.127
Recommended Citation
Ravikumar, K and Diatha, Krishna Sundar, "An actor-critic algorithm for multi-agent learning in queue-based stochastic games" (2014). Faculty Publications. 1771.
https://research.iimb.ac.in/fac_pubs/1771
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