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A reinforcement learning approach to coordinate exploration with limited communication in continuous action games

Published online by Cambridge University Press:  11 February 2016

Abdel Rodríguez ,
Peter Vrancx ,
Ricardo Grau  and
Ann Nowé
Abdel Rodríguez
Affiliation:
Computational Modeling Lab, Vrije Universiteit Brussel – Pleinlaan 2, 1050 Brussels, Belgium e-mail: [email protected], [email protected], [email protected]
Peter Vrancx
Affiliation:
Computational Modeling Lab, Vrije Universiteit Brussel – Pleinlaan 2, 1050 Brussels, Belgium e-mail: [email protected], [email protected], [email protected]
Ricardo Grau
Affiliation:
Center of Studies in Informatics, Universidad Central ‘Marta Abreu’ de Las Villas – Carretera a Camajuaní Km 5, 50100 Villa Clara, Cuba e-mail: [email protected]
Ann Nowé
Affiliation:
Computational Modeling Lab, Vrije Universiteit Brussel – Pleinlaan 2, 1050 Brussels, Belgium e-mail: [email protected], [email protected], [email protected]
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Abstract

Learning automata are reinforcement learners belonging to the class of policy iterators. They have already been shown to exhibit nice convergence properties in a wide range of discrete action game settings. Recently, a new formulation for a continuous action reinforcement learning automata (CARLA) was proposed. In this paper, we study the behavior of these CARLA in continuous action games and propose a novel method for coordinated exploration of the joint-action space. Our method allows a team of independent learners, using CARLA, to find the optimal joint action in common interest settings. We first show that independent agents using CARLA will converge to a local optimum of the continuous action game. We then introduce a method for coordinated exploration which allows the team of agents to find the global optimum of the game. We validate our approach in a number of experiments.

Type
Articles
Information
The Knowledge Engineering Review , Volume 31 , Issue 1: Adaptive Learning Agents , January 2016 , pp. 77 - 95
Copyright
© Cambridge University Press, 2016 

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