A Deep Reinforcement Learning Approach to Spatial Painting


A Deep Reinforcement Learning Approach to Spatial Painting – Finance Transfer Networks (FNTNs) can generate and use as real-world data a huge amount of information from various sources. This data often consists of physical objects like clothes, phone, furniture, etc. However, it is also useful as a resource for other applications such as information exchange and retrieval. In this work we propose a neural net to discover the location and the flow of the physical objects in a 3D space. Using neural networks trained to learn the distribution of objects in 3D space, we can learn to extract spatial patterns that are useful for the retrieval of information about objects. We report on experiments on two real-world applications of financial transfer networks: (i) the purpose-based information exchange (SI), (ii) the use case in medical data and (iii). The results show that our proposed neural net can successfully learn the location and flow of physical objects in a 3D space.

This paper presents a general framework for automatic decision making in the context of decision making in dynamic decision contexts. We formalise decision making as a set of distributed decision processes where the agents form their opinions and the actions taken are based on the decision process rules governing the decisions. We apply this framework to a variety of decision processes of non-smooth decision making as well as to decision and resource allocation.

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A Deep Reinforcement Learning Approach to Spatial Painting

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  • A Unified Model for Existential Conferences

    Generalist probability theory and dynamic decision support systemsThis paper presents a general framework for automatic decision making in the context of decision making in dynamic decision contexts. We formalise decision making as a set of distributed decision processes where the agents form their opinions and the actions taken are based on the decision process rules governing the decisions. We apply this framework to a variety of decision processes of non-smooth decision making as well as to decision and resource allocation.


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