Elevator Group Control Using Multiple Reinforcement Learning Agents
Machine Learning
An overlay smart spaces system for load balancing in wireless LANs
Mobile Networks and Applications
Automatic shaping and decomposition of reward functions
Proceedings of the 24th international conference on Machine learning
A framework for meta-level control in multi-agent systems
Autonomous Agents and Multi-Agent Systems
Cooperative content distribution and traffic engineering in an ISP network
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Reinforcement learning for vulnerability assessment in peer-to-peer networks
IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
Emergent consensus in decentralised systems using collaborative reinforcement learning
Self-star Properties in Complex Information Systems
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In this paper we describe a self-adjusting algorithm for packet routing, in which a reinforcement learning module is embedded into each node of a switching network. Only local communication is used to keep accurate statistics at each node on which routing policies lead to minimal delivery times. In simple experiments involving a 36-node, irregularly connected network, this learning approach proves superior to a nonadaptive algorithm based on precomputed shortest paths.