Using collective intelligence to route Internet traffic
Proceedings of the 1998 conference on Advances in neural information processing systems II
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Self-organization through bottom-up coalition formation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Decentralized Markov Decision Processes with Event-Driven Interactions
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Learning the task allocation game
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Multiagent reinforcement learning and self-organization in a network of agents
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
An Application of Automated Negotiation to Distributed Task Allocation
IAT '07 Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Using quantitative models to search for appropriate organizational designs
Autonomous Agents and Multi-Agent Systems
Integrating organizational control into multi-agent learning
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Networked distributed POMDPs: a synthesis of distributed constraint optimization and POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Average-reward decentralized Markov decision processes
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A multi-agent learning approach to online distributed resource allocation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Cognitive policy learner: biasing winning or losing strategies
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Coordinating multi-agent reinforcement learning with limited communication
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Engineering Applications of Artificial Intelligence
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Decentralized reinforcement learning (DRL) has been applied to a number of distributed applications. However, one of the main challenges faced by DRL is its convergence. Previous work has shown that hierarchically organizational control is an effective way of coordinating DRL to improve its speed, quality, and likelihood of convergence. In this paper, we develop a distributed, negotiation-based approach to dynamically forming such hierarchical organizations. To reduce the complexity of coordinating DRL, our self-organization approach groups strongly-interacting learning agents together, whose exploration strategies are coordinated by one supervisor. We formalize this idea by characterizing interactions among agents in a decentralized Markov Decision Process model and defining and analyzing a measure that explicitly captures the strength of such interactions. Experimental results show that our dynamically evolving organizations outperform predefined organizations for coordinating DRL.