Transfer Learning based Agent for Automated Negotiation

Although great success has been made in automated negotiation, a major issue still stands out: it is inefficient that learning a policy from scratch when an agent encounters an unknown opponent. Transfer learning (TL) can alleviate this problem by utilizing the knowledge of previously learned policies to accelerate the current task learning. This work presents a novel Transfer Learning-based Negotiating Agent (TLNAgent) framework that allows an autonomous agent to transfer previous knowledge from source policies to help with new tasks, while boosting its performance. TLNAgent comprises three key components: the negotiation module, the adaptation module and the transfer module. Specifically, the negotiation module is responsible for interacting with the other agent during negotiation. The adaptation module measures the helpfulness of each source policy based on a fusion of two selection mechanisms. The transfer module is based on lateral connections between source and target networks and accelerates the agent's training by transferring knowledge from the selected source policy. Our comprehensive experiments clearly demonstrate that TL is effective in the context of automated negotiation, and \name outperforms state-of-the-art negotiating agents in various domains.