Multi-Objective Reinforcement Learning for Designing Ethical Multi-Agent Environments
This paper tackles the open problem of value alignment in multi-agent systems. In particular, we propose an approach to build an ethical environment that guarantees that agents in the system learn a joint ethically-aligned behaviour while pursuing their respective individual objectives. Our contribu...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/224349 |
| Acceso en línea: | https://hdl.handle.net/2445/224349 |
| Access Level: | acceso abierto |
| Palabra clave: | Ètica Aprenentatge per reforç (Intel·ligència artificial) Sistemes multiagent Ethics Reinforcement learning Multiagent systems |
| Sumario: | This paper tackles the open problem of value alignment in multi-agent systems. In particular, we propose an approach to build an ethical environment that guarantees that agents in the system learn a joint ethically-aligned behaviour while pursuing their respective individual objectives. Our contributions are founded in the framework of Multi-Objective Multi-Agent Reinforcement Learning. Firstly, we characterise a family of Multi-Objective Markov Games (MOMGs), the socalled ethical MOMGs, for which we can formally guarantee the learning of ethical behaviours. Secondly, based on our characterisation we specify the process for building single-objective ethical environments that simplify the learning in the multi-agent system. We illustrate our process with an ethical variation of the Gathering Game, where agents manage to compensate social inequalities by learning to behave in alignment with the moral value of beneficence. |
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