Automated ethical design of multi-agent reinforcement learning environments
This paper introduces the Approximate Multi-Agent Ethical Embedding Process, an algorithm to ethically design reinforcement learning environments where agents learn behaviours aligned with a moral value, while pursuing their own goals. Building on Multi-Objective and Deep Reinforcement Learning, it...
| Autores: | , , , , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2025 |
| País: | España |
| Recursos: | Universitat Pompeu Fabra |
| Repositório: | Repositorio Digital de la UPF |
| OAI Identifier: | oai:dnet:rdupf_______::b3d0de3f397dfd3193c8d38517418114 |
| Acesso em linha: | https://hdl.handle.net/10230/73324 http://dx.doi.org/10.3233/FAIA250587 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Multi-agent reinforcement learning Value-alignment Multi-objective Reinforcement learning |
| Resumo: | This paper introduces the Approximate Multi-Agent Ethical Embedding Process, an algorithm to ethically design reinforcement learning environments where agents learn behaviours aligned with a moral value, while pursuing their own goals. Building on Multi-Objective and Deep Reinforcement Learning, it extends a previously theory-driven method limited to small-scale problems. The new approach is tested in a scaled-up, ethically augmented version of the gathering game, demonstrating its effectiveness in managing increased complexity. |
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