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arXiv · 1401.3907

Policy Invariance under Reward Transformations for General-Sum Stochastic Games

Xiaosong Lu·Howard M. Schwartz·Sidney N. Givigi Jr
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Abstract

We extend the potential-based shaping method from Markov decision processes to multi-player general-sum stochastic games. We prove that the Nash equilibria in a stochastic game remains unchanged after potential-based shaping is applied to the environment. The property of policy invariance provides a possible way of speeding convergence when learning to play a stochastic game.

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Xiaosong Lu, Howard M. Schwartz, Sidney N. Givigi Jr. 2014-01-16. Policy Invariance under Reward Transformations for General-Sum Stochastic Games. https://doi.org/10.1613/jair.3384

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