Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Distributed agent-based air traffic flow management
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Analyzing and visualizing multiagent rewards in dynamic and stochastic domains
Autonomous Agents and Multi-Agent Systems
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Learning in multiagent systems is generally slow because the agent has to extract its correct policy through not only through its interaction with the environment, but also from its interactions with other learning agents. In this paper, we present an approach that significantly improves the learning speed in multiagent systems by allowing an agent to up-date its estimate of the rewards for all its available actions, not just the action that was taken. Our results show that the rewards on such "actions not taken" are beneficial early in training, particularly when agent teams are leveraged to estimate those rewards.