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A reinforcement learning optimized negotiation method based on mediator agent
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DOI：10.1016/j.eswa.2014.06.003.png)
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Abstract
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This paper firstly proposes a bilateral optimized negotiation model based on reinforcement learning. This model negotiates on the issue price and the quantity, introducing a mediator agent as the mediation mechanism, and uses the improved reinforcement learning negotiation strategy to produce the optimal proposal. In order to further improve the performance of negotiation, this paper then proposes a negotiation method based on the adaptive learning of mediator agent. The simulation results show that the proposed negotiation methods make the efficiency and the performance of the negotiation get improved. (C) 2014 Elsevier Ltd. All rights reserved.
Keywords:
Multi-agent system
Reinforcement learning
Optimized negotiation
Mediator agent
Negotiation strategy
Adaptive learning
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