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- W4253365321 abstract "This article considers adaptive control architectures that integrate active sensory-motor systems with decision systems based on reinforcement learning. One unavoidable consequence of active perception is that the agent's internal representation often confounds external world states. We call this phoenomenon Perceptual aliasing and show that it destabilizes existing reinforcement learning algorithms with respect to the optimal decision policy. We then describe a new decision system that overcomes these difficulties for a restricted class of decision problems. The system incorporates a perceptual subcycle within the overall decision cycle and uses a modified learning algorithm to suppress the effects of perceptual aliasing. The result is a control architecture that learns not only how to solve a task but also where to focus its visual attention in order to collect necessary sensory information." @default.
- W4253365321 created "2022-05-12" @default.
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- W4253365321 date "1991-07-01" @default.
- W4253365321 modified "2023-10-06" @default.
- W4253365321 title "Learning to perceive and act by trial and error" @default.
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- W4253365321 doi "https://doi.org/10.1007/bf00058926" @default.
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