A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation
This addresses the need for adaptive automated negotiation agents in e-commerce, though it is incremental as it builds on existing reinforcement learning methods.
The paper tackles the problem of automated agents learning to negotiate in concurrent bilateral negotiations in unknown dynamic e-markets, using a deep reinforcement learning approach with actor-critic architecture and pre-training from synthetic data, resulting in agents that outperform two existing negotiation strategies across various e-market settings.
We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning-based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings.