Team-partitioned, opaque-transition reinforcement learning
Proceedings of the third annual conference on Autonomous Agents
Hierarchical multi-agent reinforcement learning
Proceedings of the fifth international conference on Autonomous agents
A Multi-Agent Policy-Gradient Approach to Network Routing
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
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
Self-organization for coordinating decentralized reinforcement learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Learning from experience to generate new regulations
COIN@AAMAS'10 Proceedings of the 6th international conference on Coordination, organizations, institutions, and norms in agent systems
Using experience to generate new regulations
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Coordination guided reinforcement learning
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Orchestrating multiagent learning of penalty games
SBIA'12 Proceedings of the 21st Brazilian conference on Advances in Artificial Intelligence
Holonic multi-agent system for traffic signals control
Engineering Applications of Artificial Intelligence
Coordinating multi-agent reinforcement learning with limited communication
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Robust Regulation Adaptation in Multi-Agent Systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Learning collaborative team behavior from observation
Expert Systems with Applications: An International Journal
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Multi-Agent Reinforcement Learning (MARL) algorithms suffer from slow convergence and even divergence, especially in large-scale systems. In this work, we develop an organization-based control framework to speed up the convergence of MARL algorithms in a network of agents. Our framework defines a multi-level organizational structure for automated supervision and a communication protocol for exchanging information between lower-level agents and higher-level supervising agents. The abstracted states of lower-level agents travel upwards so that higher-level supervising agents generate a broader view of the state of the network. This broader view is used in creating supervisory information which is passed down the hierarchy. The supervisory policy adaptation then integrates supervisory information into existing MARL algorithms, guiding agents' exploration of their state-action space. The generality of our framework is verified by its applications on different domains (distributed task allocation and network routing) with different MARL algorithms. Experimental results show that our framework improves both the speed and likelihood of MARL convergence.