Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published December 2019 | Submitted
Book Section - Chapter Open

Entropy-Regularized Stochastic Games

Abstract

In zero-sum stochastic games, where two competing players make decisions under uncertainty, a pair of optimal strategies is traditionally described by Nash equilibrium and computed under the assumption that the players have perfect information about the stochastic transition model of the environment. However, implementing such strategies may make the players vulnerable to unforeseen changes in the environment. In this paper, we introduce entropy-regularized stochastic games where each player aims to maximize the causal entropy of its strategy in addition to its expected payoff. The regularization term balances each player's rationality with its belief about the level of misinformation about the transition model. We consider both entropy-regularized N-stage and entropy-regularized discounted stochastic games, and establish the existence of a value in both games. Moreover, we prove the sufficiency of Markovian and stationary mixed strategies to attain the value, respectively, in N-stage and discounted games. Finally, we present algorithms, which are based on convex optimization problems, to compute the optimal strategies. In a numerical example, we demonstrate the proposed method on a motion planning scenario and illustrate the effect of the regularization term on the expected payoff.

Additional Information

© 2019 IEEE. This work was supported in part by the grants AFRL # FA9550-19-1-0169 and DARPA # D19AP00004.

Attached Files

Submitted - 1907.11543.pdf

Files

1907.11543.pdf
Files (358.6 kB)
Name Size Download all
md5:65bf7b734bbd78c99ab136e560ab9fe2
358.6 kB Preview Download

Additional details

Created:
August 19, 2023
Modified:
October 20, 2023