A deep reinforcement learning-based agent for negotiation with multiple communication channels

Abstract

Agent-based negotiation aims at automating negotiation process on behalf of humans to save time and efforts. While successful, the research of automated negotiation focuses on communication merely through offer exchange (e.g., following alternating offer protocol). As many real-world settings involve linguistic channel to express intentions, ask questions, discuss plans and so on, the information bandwidth is therefore restricted and grounded in the action space of negotiation. To bridge the gap, this work proposes the MCAN (Multiple Channel Automated Negotiation) agent that is based on deep reinforcement learning (DRL). It is capable of negotiating using both offers and linguistic communication channels with autonomous agents or human players. Specifically, the agent leverages Parametrized Deep Q-Network (P-DQN) that provides solution for a hybrid discrete-continuous action space, thereby learning a comprehensive negotiation strategy which integrates linguistic communication skills and bidding strategies. The experimental results show that the MCAN agent can successfully complete negotiations with both proposal and linguistic communication, and it outperforms baseline agents in terms of averaged utility.