deep reinforcement learningempirical methodsgeneralizationQ-networksDeep reinforcement learning (RL) methods have achieved remarkable performance on challenging control tasks. Observations of the resulting behavior give the impression that the agent has constructed a generalized representation that supports ...
Packer, Charles, Katelyn Gao, Jernej Kos, Philipp Krähenbühl, Vladlen Koltun, and Dawn Song. "Assessing generalization in deep reinforcement learning." arXiv preprint arXiv:1810.12282 (2018). 前言 (注意:这是一篇实验报告式的文章) 现在的RL算法很容易overfit到固定的环境上,因为他们通常都是在测试...
Bootstrap State Representations for Better Generalization in Deep... CoRL 2021 Submission 有点水,劝大家别看。 前言 vision based的RL任务需要把图像观察变成state representation然后进行训练。然而图像中有些信息是无关紧要的,比如背景的色彩之类。 为了增加RL的泛化性,人们会使用data augmentation的方法来提升训练...
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning / ICLR 2020 - pokaxpoka/netrand
Deep reinforcement learning that matters Proceedings of the AAAI Conference on Artificial Intelligence (2018) D. Bertoin et al. Look where you look! Saliency-guided Q-networks for generalization in visual reinforcement learning Adv. Neural Inf. Process. Syst. (2022) N. Hansen et al. Generalizati...
A survey of zero-shot generalisation in deep reinforcement learning. J. Artif. Intell. Res. 76, 201–264 (2023). Nature Neuroscience | Volume 26 | August 2023 | 1438–1448 1447 Article https://doi.org/10.1038/s41593-023-01382-9 46. Wei, J. et al. Emergent abilities of large ...
这里权重λ通过从贝塔分布采样得到 对应的所有的监督信号都要做相应的插值 比如PPO里面advantage和action DQN里面也同理。然后整个方法就介绍完了,就是这么简单。。 总结:感觉也太简单粗暴了,这也可以是NIPS,还是有点吃惊的,可能就是效果不错?不过这也给了我们这些人一些中NIPS的希望吧。
本文授权转载自Medium,作者Ivan Lee。 李宏毅老师通过下面的地球跟机器人比喻RL(Reinforcement Learning)过程是怎么回事。 地球是环境(environment),代理(agent)用感测器去接收外接讯息,就像无人车在路上有六种... Deep Learning Deep Learning 一. 作者简介 二. 论文的意义和主要内容 论文意义 概念 原理 应用 三...
Deep reinforcement learning has obtained impressive results in the last few years. However, the limitations of deep reinforcement learning with respect to ... D Beretta,S Monica,F Bergenti 被引量: 0发表: 2023年 Investigating latent representations and generalization in deep neural networks for tabul...
Recent work applying deep reinforcement learning (DRL) to solve traveling salesman problems (TSP) has shown that DRL-based solvers can be fast and competitive with TSP heuristics for small instances, but do not generalize well to larger instances. In this work, we propose a novel approach named...