While negotiating agents have achieved remarkable success, one critical challenge that remains unresolved is the inherent inefficiency of learning negotiation strategies from scratch when encountering previously unencountered opponents. To address this limitation, Transfer Learning (TL) emerges as a promising solution, leveraging knowledge acquired from prior tasks to accelerate learning and enhance adaptability in new negotiation contexts. This study introduces Transfer Learning-based Negotiating Agent (TLNAgent), a novel framework enabling autonomous negotiating agents to systematically leverage knowledge from pretrained source policies. The proposed transfer mechanism not only enhances negotiation performance but also substantially accelerates policy adaptation in unfamiliar negotiation environments. TLNAgent integrates three core components: (1) a negotiation module that interacts with opponents; (2) a critic module that determines whether to activate the transfer process and selects which source policies to transfer; and (3) a transfer module that facilitates knowledge integration between source and target policies. Specifically, the negotiation module interacts with opponents during the negotiation to execute core decision-making processes; in addition, it trains new policies using reinforcement learning. The critic module serves dual critical functions: (1) it dynamically triggers the transfer module according to interaction analysis; and (2) it selects the source policies via its adaptation model. The transfer module establishes lateral parameter-level connections between source and target policy networks, facilitating systematic knowledge transfer while ensuring training stability. Empirical findings from our extensive experiments indicate that transfer learning considerably enhances both the efficiency and utility of outcomes in cross-domain negotiation tasks. The proposed framework attains superior performance when compared to the state-of-the-art negotiating agents from the Automated Negotiating Agents Competition (ANAC).
Chen et al. (Tue,) studied this question.