Solving very large weakly coupled Markov decision processes
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Analyzing and visualizing multiagent rewards in dynamic and stochastic domains
Autonomous Agents and Multi-Agent Systems
Solving multiagent assignment Markov decision processes
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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In this work, we introduce a method for decentralized coordination in cooperative multiagent multi-task problems where the subtasks and agents are homogeneous. Using the method proposed, the agents cooperate at the high level task selection using the knowledge they gather by learning subtasks. We introduce a subtask selection method for single agent multi-task MDPs and we extend the work to multiagent multi-task MDPs by using reward shaping at the subtask level to coordinate the agents. Our results on a multi-rover problem show that agents which use the combination of task decomposition and subtask based difference rewards result in significant improvement both in terms of learning speed, and converged policies.