Extracting explanations about cooperation in a multi-agent reinforcement learning scenario
Reinforcement learning is a Machine Learning technique where an agent learns how to interact in an dynamic environment by performing actions and getting rewards or penalization as a feedback that will point how good or bad was the performed action. Many methods currently used in reinforcement learni...
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| Tipo de documento: | dissertação |
| Data de publicação: | 2023 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositório: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglês |
| OAI Identifier: | oai:upcommons.upc.edu:2117/396883 |
| Acesso em linha: | https://hdl.handle.net/2117/396883 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Reinforcement learning reinforcement-learning multi-agent explainability black-box environment actions reward Aprenentatge per reforç Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Resumo: | Reinforcement learning is a Machine Learning technique where an agent learns how to interact in an dynamic environment by performing actions and getting rewards or penalization as a feedback that will point how good or bad was the performed action. Many methods currently used in reinforcement learning produce black-box models that are difficult to interpret, leading to unpredictable behavior and lack of trust on trained agents. Explainability methods can be used to tackle these issues, but they are usually applied on single-agent settings. We will explore the feasibility of applying a chosen method in a multi-agent setting with agents trained by us and we will explore how the explanations take into account the agent coordination aspects. |
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