Reinforcement learning with utility-aware agents for market-based resource allocation

Authors:
Eduardo Rodrigues Gomes;Ryszard Kowalczyk
Affiliations:
Swinburne University of Technology, Victoria, Australia;Swinburne University of Technology, Victoria, Australia
Venue:
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Year:
2007

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Abstract

In this paper we propose and investigate the use of Reinforcement Learning in a market-based resource allocation mechanism called Iterative Price Adjustment. Under standard assumptions, this mechanism uses demand functions that do not allow the agents to have preferences over the attributes of the allocation, e.g. the price of the resources. To address this limitation, we study the case where the agent's preferences in the resource allocation are described by utility functions and they learn the demand functions given their utility functions. The approach has been evaluated with extensive experiments.