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
Scaling model-based average-reward reinforcement learning for product delivery
ECML'06 Proceedings of the 17th European conference on Machine Learning
Learning potential functions and their representations for multi-task reinforcement learning
Autonomous Agents and Multi-Agent Systems
Hi-index | 0.00 |
Difference rewards (a particular instance of reward shaping) have been used to allow multiagent domains to scale to large numbers of agents, but they remain difficult to compute in many domains. We present an approach to modeling the global reward using function approximation that allows the quick computation of shaped difference rewards. We demonstrate how this model can result in significant improvements in behavior for two air traffic control problems. We show how the model of the global reward may be either learned on- or off-line using a linear combination of neural networks.