Using Neuroevolved Binary Neural Networks to solve reinforcement learning environments
Using Neuroevolved Binary Neural Networks to solve reinforcement learning environments
复制标题
使用神经进化二元神经网络解决强化学习环境
DOI：
10.1109/apccas47518.2019.8953134
复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
O. Sinnen
中科院分区：
文献类型：
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作者：
Raul Valencia;Chiu;O. Sinnen
随着对神经网络利用的爆炸性兴趣，已经采取了几种方法，以使其更快，更准确或有效。用于简化推理模型的一种技术是利用二进制表示重量，激活，输入和输出。本文提出了一种新颖的方法，可以使用神经进化作为其基本技术（无梯度下降）来训练从头开始二进制神经网络，然后将这些结果应用于OpenAI体育馆在OpenAI体育馆中测试的标准增强学习环境。结果和代码可以在https://github.com/rval735/bisuna中找到。
With the explosive interest in the utilization of Neural Networks, several approaches have taken place to make them faster, more accurate or power efficient; one technique used to simplify inference models is the utilization of binary representations for weights, activations, inputs and outputs. This paper presents a novel approach to train from scratch Binary Neural Networks using neuroevolution as its base technique (gradient descent free), to then apply such results to standard Reinforcement Learning environments tested in the OpenAI Gym. The results and code can be found in https://github.com/rval735/BiSUNA.