Transfer of Reinforcement Learning Negotiation Policies: from Bilateral to Multilateral Scenarios

Trading and negotiation dialogue capabilities have been identified as important in a variety of AI application areas. In prior work, it was shown how Reinforcement Learning (RL) agents in bilateral negotiations can learn to use manipulation in dialogue to deceive adversaries in non-cooperative trading games. In this paper we show that such trained policies can also be used effectively for multilateral negotiations, and can even outperform those which are trained in these multilateral environments. Ultimately, it is shown that training in simple bilateral environments (e.g. a generic version of "Catan") may suffice for complex multilateral non-cooperative trading scenarios (e.g. the full version of Catan).