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Tunable Dynamics in Agent-Based Simulation using Multi-Objective Reinforcement Learning
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
2019 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

Agent-based simulation is a powerful tool for studying complex systems of interacting agents. To achieve good results, the behavior models used for the agents must be of high quality. Traditionally these models have been handcrafted by domain experts. This is a difficult, expensive and time consuming process. In contrast, reinforcement learning allows agents to learn how to achieve their goals by interacting with the environment. However, after training the behavior of such agents is often static, i.e. it can no longer be affected by a human. This makes it difficult to adapt agent behavior to specific user needs, which may vary among different runs of the simulation. In this paper we address this problem by studying how multi-objective reinforcement learning can be used as a framework for building tunable agents, whose characteristics can be adjusted at runtime to promote adaptiveness and diversity in agent-based simulation. We propose an agent architecture that allows us to adapt popular deep reinforcement learning algorithms to multi-objective environments. We empirically show that our method allows us to train tunable agents that can approximate the policies of multiple species of agents.

Place, publisher, year, edition, pages
2019. p. 1-7
Keywords [en]
Modelling for agent based simulation, Reward structures for learning, Learning agent capabilities (agent models, communication, observation)
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-161093OAI: oai:DiVA.org:liu-161093DiVA, id: diva2:1362933
Conference
Adaptive and Learning Agents Workshop (ALA-19) at AAMAS, Montreal, Canada, May 13-14, 2019
Funder
Vinnova, 2017-04885Wallenberg AI, Autonomous Systems and Software Program (WASP)Available from: 2019-10-22 Created: 2019-10-22 Last updated: 2019-10-23Bibliographically approved

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CiteExportLink to record
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Citation style
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