Reinforcement Learning with Raw Image Pixels as Input State

机译：以原始图像像素为输入状态的强化学习

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摘要

We report in this paper some positive simulation results obtained when image pixels are directly used as input state of a reinforcement learning algorithm. The reinforcement learning algorithm chosen to carry out the simulation is a batch-mode algorithm known as fitted Q iteration.

机译：我们在本文中报告了当图像像素直接用作增强学习算法的输入状态时获得的一些积极的仿真结果。选择进行模拟的强化学习算法是一种称为拟合Q迭代的批处理模式算法。

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来源
《International Workshop on Intelligent Computing in Pattern Analysis/Synthesis(IWICPAS 2006); 20060826-27; Xi'an(CN)》|2006年|P.446-454|共9页
会议地点 Xian(CN)
作者
Damien Ernst; Raphael Maree; Louis Wehenkel;
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作者单位

Postdoctoral Researcher FNRS;

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原文格式 PDF
正文语种 eng
中图分类信息处理（信息加工）;
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4. Reinforcement Learning with Raw Image Pixels as Input State [C] . Damien Ernst, Raphael Maree, Louis Wehenkel International Workshop on Intelligent Computing in Pattern Analysis/Synthesis . 2006

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6. Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network for Pixel-by-Pixel Image Translation [O] . A. Hagiwara, Y. Otsuka, M. Hori, 2019

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8. Imer-product array processor for retrieval of stored images represented by bipolar binary (1,-1) pixels using partial input trinary pixels represented by (1,-1) [R] . 1993

机译：Imer-product数据处理器，用于使用由（1，-1）表示的部分输入三进制像素检索由双极二进制（1，-1）像素表示的存储图像

Reinforcement Learning with Raw Image Pixels as Input State

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