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A Reinforcement Learning Approach to Coordinate Exploration with Limited Communication in Continuous Action Games This publication appears in: Knowledge Engineering Review Authors: A. Rodriguez, P. Vrancx and A. Nowé Volume: 32 Number of Pages: 19 Publication Year: 2014
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 ?nd 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.
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