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Successful negotiators must learn how to balance optimizing for self-interestand cooperation. Yet current artificial negotiation agents often heavily dependon the quality of the static datasets they were trained on, limiting theircapacity to fashion an adaptive response balancing self-interest andcooperation. For this reason, we find that these agents can achieve either highutility or cooperation, but not both. To address this, we introduce a targeteddata acquisition framework where we guide the exploration of a reinforcementlearning agent using annotations from an expert oracle. The guided explorationincentivizes the learning agent to go beyond its static dataset and develop newnegotiation strategies. We show that this enables our agents to obtainhigher-reward and more Pareto-optimal solutions when negotiating with bothsimulated and human partners compared to standard supervised learning andreinforcement learning methods. This trend additionally holds when comparingagents using our targeted data acquisition framework to variants of agentstrained with a mix of supervised learning and reinforcement learning, or toagents using tailored reward functions that explicitly optimize for utility andPareto-optimality.

Targeted Data Acquisition for Evolving Negotiation Agents