reinforce- ment learning algorithms bring the hope for machines to have the human-like abilities by directly learning dexterous manipulation from raw pixels. In this review paper, we address the current status of reinforcement learning algorithms used in the field. We...
In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cog...
论文的主要焦点是合作多智能体深度强化学习(Cooperative Multi-Agent Deep Reinforcement Learning)。它主要讨论了五种常见的解决合作多智能体强化学习问题的方法:独立学习者、全观察批评者、价值函数分解、共识和学习通信。论文还提供了这些类别中最近论文的概览,包括问题设置、关键思想、算法的主要步骤,以及用于评估算法性能...
Reinforcement learningis the task of learning what actions to take, given a certain situation/environment, so as to maximize a reward signal. The interesting difference between supervised and reinforcement learning is that this reward signal simply tells you whether the action (or input) that the a...
Deep Reinforcement Learning has made significant progress in multi-agent systems in recent years. The aim of this review article is to provide an overview of recent approaches on Multi-Agent Reinforcement Learning (MARL) algorithms. Our classification of MARL approaches includes five categories for mod...
As a general artificial intelligence technology, deep reinforcement learning (DRL) is promising to address the above challenges. Notably, the recent years have seen the surge of DRL for SBEM. However, there lacks a systematic overview of different DRL methods for SBEM. To fill the gap, this ...
Jiang Y, Shin H, Ko H (2018) Precise regression for bounding box correction for improved tracking based on deep reinforcement learning. In: 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 1643–1647 Ren L, Lu J, Wang Z, Tian Q, Zhou J (...
A Review of Deep Reinforcement Learning Methods and Military Application Research In the area of artificial intelligence, deep reinforcement learning has grown in significance. It has accomplished extraordinary feats and offers a fresh a... N Wang,Z Li,X Liang,... - 《Mathematical Problems in Eng...
Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works ha
We describe current shortcomings, enhancements, and implementations. The review also covers different types of deep architectures, such as deep convolution networks, deep residual networks, recurrent neural networks, reinforcement learning, variational autoencoders, and others. 展开 ...