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This paper addresses the inherent complexity in coordinating learned behavioral strategies of multiple agents working towards a common goal. Because of the interactions among the agents, a primary challenge of policy learning is escalating computational complexity with increasing number of agents and the size of the task space (including action choices and world states). We employ an approach that...
In many practical robotics problems, knowledge of the team configuration and capabilities is crucial in coordination of multiple heterogeneous robots. In a challenging environment with costly, sporadic, or absent communication, inferencing based on observed spatio-temporal state transitions is necessary for learning and reasoning. In this paper, we present a general purpose inference engine that takes...
In the area of autonomous multi-robot cooperation, much emphasis has been placed on how to coordinate individual robot behaviors in order to achieve an optimal solution to task completion as a team. This paper presents an approach to cooperative multi-robot reinforcement learning based on a hybrid state space representation of the environment to achieve both task learning and heterogeneous role emergence...
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