2.2.5 Reinforcement Learning: 这里用的就是 DQN 算法,具体可以参考其他博客。 2.3 Cross-lingual policy transfer: 这里进行跨语言策略迁移的目的是:为了处理数据量比较少的语言中的 active learning 问题。作者采用 Transfer learning 的方法,在数据量丰富的数据集上学习一个比较好的 policy,然后将这种策略应用到 数...
2.2.5 Reinforcement Learning: 这里用的就是 DQN 算法,具体可以参考其他博客。 2.3 Cross-lingual policy transfer: 这里进行跨语言策略迁移的目的是:为了处理数据量比较少的语言中的 active learning 问题。作者采用 Transfer learning 的方法,在数据量丰富的数据集上学习一个比较好的 policy,然后将这种策略应用到 数...
In this paper we design and evaluate a Deep-Reinforcement Learning agent that optimizes routing. Our agent adapts automatically to current traffic conditions and proposes tailored configurations that attempt to minimize the network delay. Experiments show very promising performance. Moreover, this approac...
This paper aims to examine the potential of using the emerging deep reinforcement learning techniques in missile guidance applications. To this end, a Markovian decision process that enables the application of reinforcement learning theory to solve the guidance problem is formulated. A heuristic way is...
云计算:A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud 论文主要内容翻译 预测自动扩展(Predictive Autoscaling)是一种重要的机制,支持根据云中波动的工作负载需求自主调整计算资源。在最近的研究中,强化学习(Reinforcement Learning, RL)作为一种有前途的方法,用于学习资源管理策略,以指导...
2. Deep RL in 行为决策 和 运动规划 典型的pipeline是,输入传感器数据流,辅以全局路径规划信息,处理后最终得到控制输出(转角、加速度),这种处理的流程一般是分层的,因为驾驶动作天然是分级的,先是一个高级的离散状态的决策(行为决策,换道、跟车、左转),接着一个连续状态空间的动作(运动规划,提供能满足behavior的...
The main contribution of this letter is to develop a deep reinforcement learning-based control-aware scheduling (DEEPCAS) algorithm to tackle these issues. We use the following (optimal) design strategy: first, we synthesize an optimal controller for each subsystem; next, we design a learning ...
Deep reinforcement learning (DRL)Industrial Internet of Things (IIoT)Finding a vacant parking slot in densely populated areas leads to excessive emission of Carbon Dioxide, fuel, and time wastage. Recently, the Industrial Internet of Things (IIoT) has shown significant potential to strengthen the ...
今天阅读了一篇论文,题目叫《DRN: A Deep Reinforcement Learning Framework for News Recommendation》。该论文便是深度强化学习和推荐系统的一个结合,也算是提供了一个利用强化学习来做推荐的完整的思路和方法吧。本文便是对文章中的内容的一个简单的介绍,希望对大家有所启发。
Double Q-Network:思路并不新鲜,仿照Double Q-learning,一个Q网络用于选择动作,另一个Q网络用于评估动作,交替工作,解决upward-bias问题,效果不错。三个臭皮匠顶个诸葛亮么,就像工作中如果有double-check,犯错的概率就能平方级别下降。Silver15年论文Deep Reinforcement Learning with Double Q-learning ...