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Title: Model-Free Reinforcement Learning for Stochastic Parity Games
This paper investigates the use of model-free reinforcement learning to compute the optimal value in two-player stochastic games with parity objectives. In this setting, two decision makers, player Min and player Max, compete on a finite game arena - a stochastic game graph with unknown but fixed probability distributions - to minimize and maximize, respectively, the probability of satisfying a parity objective. We give a reduction from stochastic parity games to a family of stochastic reachability games with a parameter ε, such that the value of a stochastic parity game equals the limit of the values of the corresponding simple stochastic games as the parameter ε tends to 0. Since this reduction does not require the knowledge of the probabilistic transition structure of the underlying game arena, model-free reinforcement learning algorithms, such as minimax Q-learning, can be used to approximate the value and mutual best-response strategies for both players in the underlying stochastic parity game. We also present a streamlined reduction from 1 1/2-player parity games to reachability games that avoids recourse to nondeterminism. Finally, we report on the experimental evaluations of both reductions  more » « less
Award ID(s):
2009022
NSF-PAR ID:
10237400
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
31st International Conference on Concurrency Theory (CONCUR 2020)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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