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  • title: Agent Modelling under Partial Observability for Deep Reinforcement Learning
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            Agent Modelling under Partial Observability for Deep Reinforcement Learning
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            Agent Modelling under Partial Observability for Deep Reinforcement Learning

            Dec 6, 2021

            Speakers

            GP

            Georgios Papoudakis

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            FC

            Filippos Christianos

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            SVA

            Stefano V. Albrecht

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            About

            Modelling the behaviours of other agents is essential for understanding how agents interact and making effective decisions. Existing methods for agent modelling commonly assume knowledge of the local observations and chosen actions of the modelled agents during execution. To eliminate this assumption, we extract representations from the local information of the controlled agent using encoder-decoder architectures. Using the observations and actions of the modelled agents during training, our mod…

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            NeurIPS 2021

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