Real-world congestion / resource management problems (e.g. crowd congestion, traffic congestion) are typically very complex and large-scale. Multiagent reinforcement learning (MARL) is a promising candidate for dealing with this emerging complexity by providing an autonomous and distributed solution to these problems. However, there are three limiting factors that affect the deployability of MARL approaches to congestion problems. These are learning time, scalability and decentralised coordination i.e. no communication between the learning agents. In this paper we introduce Resource Abstraction, an approach that addresses these challenges by allocating the available resources into abstract groups. This abstraction creates new reward functions that provide a more informative signal to the learning agents and aid the coordination amongst them.
You can download the paper from here. Please cite our work as follows:
K. Malialis, S. Devlin and D. Kudenko. Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems. In Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2016.
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