A reinforcement learning optimized negotiation method based on mediator agent

• Proposes a bilateral multi-issue optimized negotiation model based on reinforcement learning.
• Introduces a mediator agent as the mediation mechanism.
• Uses the improved reinforcement learning negotiation strategy to produce the optimal proposal.
• Introduces a benchmark concession utility function to optimize the ability of mediation of the mediator agent.

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.