Reinforcement Learning for Extended Reality: Designing Self-Play Scenarios

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2019-01-08

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A common problem for deep reinforcement learning networks is a lack of training data to learn specific tasks through generalization. In this review, we look at extended reality, a promising but often overlooked field, for training agents using reinforcement learning. We review several techniques from the literature and then synthesize the information in order to propose a recommended design. Meta learning offers an important way forward, but the agents ability to perform self-play is considered crucial for achieving successful AI. Therefore, we focus on improving self-play scenarios for teaching self-learning agents, by providing a supportive environment for improved agent-environment interaction.

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Business Intelligence and Big Data for Innovative and Sustainable Development of Organizations, Collaboration Systems and Technologies, agent, extended reality, reinforcement learning, self-play scenarios, self-learning agent

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8 pages

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Proceedings of the 52nd Hawaii International Conference on System Sciences

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Attribution-NonCommercial-NoDerivatives 4.0 International

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