【论文推荐】最新八篇强化学习相关论文—残差网络、QMIX、元学习、动态速率分配、分层强化学习、抽象概况、快速物体检测、SOM

2018 年 4 月 3 日 专知 专知内容组
【论文推荐】最新八篇强化学习相关论文—残差网络、QMIX、元学习、动态速率分配、分层强化学习、抽象概况、快速物体检测、SOM

【导读】专知内容组整理了最近八篇强化学习(Reinforcement learning)相关文章,为大家进行介绍,欢迎查看!


1.BlockDrop: Dynamic Inference Paths in Residual Networks(BlockDrop:残差网络中的动态推断路径)




作者Zuxuan Wu,Tushar Nagarajan,Abhishek Kumar,Steven Rennie,Larry S. Davis,Kristen Grauman,Rogerio Feris

摘要Very deep convolutional neural networks offer excellent recognition results, yet their computational expense limits their impact for many real-world applications. We introduce BlockDrop, an approach that learns to dynamically choose which layers of a deep network to execute during inference so as to best reduce total computation without degrading prediction accuracy. Exploiting the robustness of Residual Networks (ResNets) to layer dropping, our framework selects on-the-fly which residual blocks to evaluate for a given novel image. In particular, given a pretrained ResNet, we train a policy network in an associative reinforcement learning setting for the dual reward of utilizing a minimal number of blocks while preserving recognition accuracy. We conduct extensive experiments on CIFAR and ImageNet. The results provide strong quantitative and qualitative evidence that these learned policies not only accelerate inference but also encode meaningful visual information. Built upon a ResNet-101 model, our method achieves a speedup of 20\% on average, going as high as 36\% for some images, while maintaining the same 76.4\% top-1 accuracy on ImageNet.

期刊:arXiv, 2018年3月30日

网址

http://www.zhuanzhi.ai/document/4df79a0e7ac6a695592bb121575f330a


2.QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningQMIX:基于单调值函数因子的深度多智能体强化学习)




作者Tabish Rashid,Mikayel Samvelyan,Christian Schroeder de Witt,Gregory Farquhar,Jakob Foerster,Shimon Whiteson

摘要In many real-world settings, a team of agents must coordinate their behaviour while acting in a decentralised way. At the same time, it is often possible to train the agents in a centralised fashion in a simulated or laboratory setting, where global state information is available and communication constraints are lifted. Learning joint action-values conditioned on extra state information is an attractive way to exploit centralised learning, but the best strategy for then extracting decentralised policies is unclear. Our solution is QMIX, a novel value-based method that can train decentralised policies in a centralised end-to-end fashion. QMIX employs a network that estimates joint action-values as a complex non-linear combination of per-agent values that condition only on local observations. We structurally enforce that the joint-action value is monotonic in the per-agent values, which allows tractable maximisation of the joint action-value in off-policy learning, and guarantees consistency between the centralised and decentralised policies. We evaluate QMIX on a challenging set of StarCraft II micromanagement tasks, and show that QMIX significantly outperforms existing value-based multi-agent reinforcement learning methods.

期刊:arXiv, 2018年3月20日

网址

http://www.zhuanzhi.ai/document/c17c70fe3802166c570a0c6153c49697


3.Learning to Adapt: Meta-Learning for Model-Based Control(学习适应:基于模型控制的元学习)




作者Ignasi Clavera,Anusha Nagabandi,Ronald S. Fearing,Pieter Abbeel,Sergey Levine,Chelsea Finn

摘要Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations can cause proficient but narrowly-learned policies to fail at test time. In this work, we propose to learn how to quickly and effectively adapt online to new situations as well as to perturbations. To enable sample-efficient meta-learning, we consider learning online adaptation in the context of model-based reinforcement learning. Our approach trains a global model such that, when combined with recent data, the model can be be rapidly adapted to the local context. Our experiments demonstrate that our approach can enable simulated agents to adapt their behavior online to novel terrains, to a crippled leg, and in highly-dynamic environments.

期刊:arXiv, 2018年3月30日

网址

http://www.zhuanzhi.ai/document/a2a848a9b0ea080048e08686ef4c946c


4.Cache-Enabled Dynamic Rate Allocation via Deep Self-Transfer Reinforcement Learning(通过深度自转移强化学习来实现缓存的动态速率分配)




作者Zhengming Zhang,Yaru Zheng,Meng Hua,Yongming Huang,Luxi Yang

机构:Southeast University

摘要Caching and rate allocation are two promising approaches to support video streaming over wireless network. However, existing rate allocation designs do not fully exploit the advantages of the two approaches. This paper investigates the problem of cache-enabled QoE-driven video rate allocation problem. We establish a mathematical model for this problem, and point out that it is difficult to solve the problem with traditional dynamic programming. Then we propose a deep reinforcement learning approaches to solve it. First, we model the problem as a Markov decision problem. Then we present a deep Q-learning algorithm with a special knowledge transfer process to find out effective allocation policy. Finally, numerical results are given to demonstrate that the proposed solution can effectively maintain high-quality user experience of mobile user moving among small cells. We also investigate the impact of configuration of critical parameters on the performance of our algorithm.

期刊:arXiv, 2018年3月30日

网址

http://www.zhuanzhi.ai/document/ee889b39f1abd03fa8fd105bb824cb8d


5.Video Captioning via Hierarchical Reinforcement Learning(基于分层强化学习的视频描述生成)




作者Xin Wang,Wenhu Chen,Jiawei Wu,Yuan-Fang Wang,William Yang Wang

机构:University of California

摘要Video captioning is the task of automatically generating a textual description of the actions in a video. Although previous work (e.g. sequence-to-sequence model) has shown promising results in abstracting a coarse description of a short video, it is still very challenging to caption a video containing multiple fine-grained actions with a detailed description. This paper aims to address the challenge by proposing a novel hierarchical reinforcement learning framework for video captioning, where a high-level Manager module learns to design sub-goals and a low-level Worker module recognizes the primitive actions to fulfill the sub-goal. With this compositional framework to reinforce video captioning at different levels, our approach significantly outperforms all the baseline methods on a newly introduced large-scale dataset for fine-grained video captioning. Furthermore, our non-ensemble model has already achieved the state-of-the-art results on the widely-used MSR-VTT dataset.

期刊:arXiv, 2018年3月29日

网址

http://www.zhuanzhi.ai/document/0e06d68487c1f38c870eed320088047e


6.Deep Communicating Agents for Abstractive Summarization(深度沟通智能体的抽象概况)




作者Asli Celikyilmaz,Antoine Bosselut,Xiaodong He,Yejin Choi

机构:University of Washington

摘要We present deep communicating agents in an encoder-decoder architecture to address the challenges of representing a long document for abstractive summarization. With deep communicating agents, the task of encoding a long text is divided across multiple collaborating agents, each in charge of a subsection of the input text. These encoders are connected to a single decoder, trained end-to-end using reinforcement learning to generate a focused and coherent summary. Empirical results demonstrate that multiple communicating encoders lead to a higher quality summary compared to several strong baselines, including those based on a single encoder or multiple non-communicating encoders.

期刊:arXiv, 2018年3月28日

网址

http://www.zhuanzhi.ai/document/2cb61ebaa88eb3db3f515f9f78fa641e


7.Dynamic Zoom-in Network for Fast Object Detection in Large Images(基于动态Zoom-in网络在大图像上的快速物体检测)




作者Mingfei Gao,Ruichi Yu,Ang Li,Vlad I. Morariu,Larry S. Davis

摘要We introduce a generic framework that reduces the computational cost of object detection while retaining accuracy for scenarios where objects with varied sizes appear in high resolution images. Detection progresses in a coarse-to-fine manner, first on a down-sampled version of the image and then on a sequence of higher resolution regions identified as likely to improve the detection accuracy. Built upon reinforcement learning, our approach consists of a model (R-net) that uses coarse detection results to predict the potential accuracy gain for analyzing a region at a higher resolution and another model (Q-net) that sequentially selects regions to zoom in. Experiments on the Caltech Pedestrians dataset show that our approach reduces the number of processed pixels by over 50% without a drop in detection accuracy. The merits of our approach become more significant on a high resolution test set collected from YFCC100M dataset, where our approach maintains high detection performance while reducing the number of processed pixels by about 70% and the detection time by over 50%.

期刊:arXiv, 2018年3月27日

网址

http://www.zhuanzhi.ai/document/3f713d0b55396afaeab8803effe5cc38


8.Modeling Others using Oneself in Multi-Agent Reinforcement Learning(在多智能体强化学习中对他人进行建模)




作者Roberta Raileanu,Emily Denton,Arthur Szlam,Rob Fergus

摘要We consider the multi-agent reinforcement learning setting with imperfect information in which each agent is trying to maximize its own utility. The reward function depends on the hidden state (or goal) of both agents, so the agents must infer the other players' hidden goals from their observed behavior in order to solve the tasks. We propose a new approach for learning in these domains: Self Other-Modeling (SOM), in which an agent uses its own policy to predict the other agent's actions and update its belief of their hidden state in an online manner. We evaluate this approach on three different tasks and show that the agents are able to learn better policies using their estimate of the other players' hidden states, in both cooperative and adversarial settings.

期刊:arXiv, 2018年3月23日

网址

http://www.zhuanzhi.ai/document/630e7bc3e8897b1c5c564c86a98cafaf

-END-

专 · 知


人工智能领域主题知识资料查看获取【专知荟萃】人工智能领域26个主题知识资料全集(入门/进阶/论文/综述/视频/专家等)

同时欢迎各位用户进行专知投稿,详情请点击

诚邀】专知诚挚邀请各位专业者加入AI创作者计划了解使用专知!

请PC登录www.zhuanzhi.ai或者点击阅读原文,注册登录专知,获取更多AI知识资料

请扫一扫如下二维码关注我们的公众号,获取人工智能的专业知识!

请加专知小助手微信(Rancho_Fang),加入专知主题人工智能群交流加入专知主题群(请备注主题类型:AI、NLP、CV、 KG等)交流~

点击“阅读原文”,使用专知!

登录查看更多
7

相关内容

强化学习(RL)是机器学习的一个领域,与软件代理应如何在环境中采取行动以最大化累积奖励的概念有关。除了监督学习和非监督学习外,强化学习是三种基本的机器学习范式之一。 强化学习与监督学习的不同之处在于,不需要呈现带标签的输入/输出对,也不需要显式纠正次优动作。相反,重点是在探索(未知领域)和利用(当前知识)之间找到平衡。 该环境通常以马尔可夫决策过程(MDP)的形式陈述,因为针对这种情况的许多强化学习算法都使用动态编程技术。经典动态规划方法和强化学习算法之间的主要区别在于,后者不假设MDP的确切数学模型,并且针对无法采用精确方法的大型MDP。

知识荟萃

精品入门和进阶教程、论文和代码整理等

更多

查看相关VIP内容、论文、资讯等

Very deep convolutional neural networks offer excellent recognition results, yet their computational expense limits their impact for many real-world applications. We introduce BlockDrop, an approach that learns to dynamically choose which layers of a deep network to execute during inference so as to best reduce total computation without degrading prediction accuracy. Exploiting the robustness of Residual Networks (ResNets) to layer dropping, our framework selects on-the-fly which residual blocks to evaluate for a given novel image. In particular, given a pretrained ResNet, we train a policy network in an associative reinforcement learning setting for the dual reward of utilizing a minimal number of blocks while preserving recognition accuracy. We conduct extensive experiments on CIFAR and ImageNet. The results provide strong quantitative and qualitative evidence that these learned policies not only accelerate inference but also encode meaningful visual information. Built upon a ResNet-101 model, our method achieves a speedup of 20\% on average, going as high as 36\% for some images, while maintaining the same 76.4\% top-1 accuracy on ImageNet.

0
5
下载
预览
小贴士
相关资讯
强化学习三篇论文 避免遗忘等
CreateAMind
11+阅读 · 2019年5月24日
逆强化学习-学习人先验的动机
CreateAMind
5+阅读 · 2019年1月18日
相关VIP内容
专知会员服务
56+阅读 · 2020年5月19日
专知会员服务
41+阅读 · 2020年3月22日
专知会员服务
34+阅读 · 2020年3月19日
专知会员服务
79+阅读 · 2020年2月1日
专知会员服务
51+阅读 · 2020年1月20日
【强化学习资源集合】Awesome Reinforcement Learning
专知会员服务
41+阅读 · 2019年12月23日
强化学习最新教程,17页pdf
专知会员服务
55+阅读 · 2019年10月11日
MIT新书《强化学习与最优控制》
专知会员服务
116+阅读 · 2019年10月9日
相关论文
Davide Abati,Jakub Tomczak,Tijmen Blankevoort,Simone Calderara,Rita Cucchiara,Babak Ehteshami Bejnordi
5+阅读 · 2020年3月31日
Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation
Risto Vuorio,Shao-Hua Sun,Hexiang Hu,Joseph J. Lim
21+阅读 · 2019年10月30日
Clustered Object Detection in Aerial Images
Fan Yang,Heng Fan,Peng Chu,Erik Blasch,Haibin Ling
4+阅读 · 2019年8月27日
Filippos Kokkinos,Stamatios Lefkimmiatis
3+阅读 · 2018年11月29日
Adrià Garriga-Alonso,Laurence Aitchison,Carl Edward Rasmussen
4+阅读 · 2018年8月16日
Bi Li,Wenxuan Xie,Wenjun Zeng,Wenyu Liu
6+阅读 · 2018年6月19日
Mohammadreza Zolfaghari,Kamaljeet Singh,Thomas Brox
5+阅读 · 2018年5月7日
Zuxuan Wu,Tushar Nagarajan,Abhishek Kumar,Steven Rennie,Larry S. Davis,Kristen Grauman,Rogerio Feris
5+阅读 · 2018年3月30日
Mingfei Gao,Ruichi Yu,Ang Li,Vlad I. Morariu,Larry S. Davis
19+阅读 · 2018年3月27日
Ali Diba,Mohsen Fayyaz,Vivek Sharma,Amir Hossein Karami,Mohammad Mahdi Arzani,Rahman Yousefzadeh,Luc Van Gool
8+阅读 · 2017年11月22日
Top