InforLorV4, Main, Merge, bibRecord, 005C04

Cooperation in stochastic games through communication

Identifieur interne : 005C04 ( Main/Merge ); précédent : 005C03; suivant : 005C05

Cooperation in stochastic games through communication

Auteurs : Raghav Aras [France] ; Alain Dutech [France] ; François Charpillet [France]

Source :

RBID : Hal:inria-00000208

Abstract

We describe a process of reinforcement learning in two-agent general-sum stochastic games under imperfect observability of moves and payoffs. In practice, it is known that using naive Q-learning, agents can learn equilibrium policies under the discounted reward criterion although these may be arbitrarily worse for both the agents than a non-equilibrium policy, in the absence of global optima. We aim for Pareto-efficiency in policies, in which agents enjoy higher payoffs than in an equilibrium and show agents may employ naive Q-learning with the addition of communication and a payoff interpretation rule, to achieve this. In principle, our objective is to shift the focus of the learning from equilibria (to which solipsistic algorithms converge) to non-equilibria by transforming the latter to equilibria.

Url:

https://hal.inria.fr/inria-00000208

DOI: 10.1145/1082473.1082691

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Le document en format XML

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<front><div type="abstract" xml:lang="en">We describe a process of reinforcement learning in two-agent general-sum stochastic games under imperfect observability of moves and payoffs. In practice, it is known that using naive Q-learning, agents can learn equilibrium policies under the discounted reward criterion although these may be arbitrarily worse for both the agents than a non-equilibrium policy, in the absence of global optima. We aim for Pareto-efficiency in policies, in which agents enjoy higher payoffs than in an equilibrium and show agents may employ naive Q-learning with the addition of communication and a payoff interpretation rule, to achieve this. In principle, our objective is to shift the focus of the learning from equilibria (to which solipsistic algorithms converge) to non-equilibria by transforming the latter to equilibria.</div>
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Cooperation in stochastic games through communication

Cooperation in stochastic games through communication

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