A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation

• URL: http://arxiv.org/abs/2001.11785v2
• Date: Mon, 3 Feb 2020 13:42:44 GMT
• Title: A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation
• Authors: Pallavi Bagga, Nicola Paoletti, Bedour Alrayes, Kostas Stathis
• Abstract summary: 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. 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.
• Score: 6.484413431061962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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