Lessons learned in single-agent and multiagent learning with robot foraging

Abstract

Multiagent learning is deeply rooted in single-agent learning. It is common thought that multiagent learning has a better result than single-agent learning with communication and knowledge sharing. This paper gives a different result in the robot foraging domain with multiagent and single-agent reinforcement learning methods. We show how a single-agent reinforcement learning method performs better than various multiagent reinforcement learning methods. Thus we propose a hypothesis: In normal robot foraging tasks with reinforcement learning, single-agent reinforcement learning is better that any multiagent reinforcement learning.