MADDM: Multi-Advisor Dynamic Binary Decision-Making by Maximizing the Utility

no code implementations • 15 May 2023 • Zhaori Guo, Timothy J. Norman, Enrico H. Gerding

In practice, however, gathering answers from a set of advisors has a cost.

Decision Making

Multi-trainer Interactive Reinforcement Learning System

no code implementations • 14 Oct 2022 • Zhaori Guo, Timothy J. Norman, Enrico H. Gerding

In this paper, we propose a more effective interactive reinforcement learning system by introducing multiple trainers, namely Multi-Trainer Interactive Reinforcement Learning (MTIRL), which could aggregate the binary feedback from multiple non-perfect trainers into a more reliable reward for an agent training in a reward-sparse environment.

reinforcement-learning Reinforcement Learning +1

From Intelligent Agents to Trustworthy Human-Centred Multiagent Systems

no code implementations • 5 Oct 2022 • Mohammad Divband Soorati, Enrico H. Gerding, Enrico Marchioni, Pavel Naumov, Timothy J. Norman, Sarvapali D. Ramchurn, Bahar Rastegari, Adam Sobey, Sebastian Stein, Danesh Tarpore, Vahid Yazdanpanah, Jie Zhang

The Agents, Interaction and Complexity research group at the University of Southampton has a long track record of research in multiagent systems (MAS).