Reinforcement learning negotiation strategy based on opponent classification

To help negotiation agent select its best actions and reach its final goal, this paper proposes a reinforcement learning negotiation strategy based on opponent classification. In the middle of negotiation process, negotiation agent makes the best use of the opponent's negotiation history to make a decision of the opponent's type, dynamically adjust the negotiation agent's belief of opponent in time, and get more favorable and better negotiation result. Finally, the algorithm is proved to be effective and practical by experiment.