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.
Original language | English |
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Pages | 297-303 |
Number of pages | 7 |
DOIs | |
Publication status | Published - Jul 2020 |
Event | 29th International Joint Conference on Artificial Intelligence - Yokohama, Japan Duration: 11 Jul 2020 → 17 Jul 2020 https://ijcai20.org |
Conference
Conference | 29th International Joint Conference on Artificial Intelligence |
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Country/Territory | Japan |
City | Yokohama |
Period | 11/07/20 → 17/07/20 |
Internet address |