Results 71 to 80 of about 1,056,132 (188)

Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications

IEEE Access, 2020
Predicting crop yield based on the environmental, soil, water and crop parameters has been a potential research topic. Deep-learning-based models are broadly used to extract significant crop features for prediction. Though these methods could resolve the
Dhivya Elavarasan, P. M. Durairaj Vincent   +1 more
doaj   +1 more source

Deep Reinforcement Factorization Machines: A Deep Reinforcement Learning Model with Random Exploration Strategy and High Deployment Efficiency

Applied Sciences, 2022
In recent years, the recommendation system and robot learning are undoubtedly the two most popular application fields, and the core algorithms supporting these two fields are deep learning based on perception and reinforcement learning based on ...
Huaidong Yu, Jian Yin
doaj   +1 more source

Deep Reinforcement Learning with Decorrelation

CoRR, 2019
Learning an effective representation for high-dimensional data is a challenging problem in reinforcement learning (RL). Deep reinforcement learning (DRL) such as Deep Q networks (DQN) achieves remarkable success in computer games by learning deeply encoded representation from convolution networks.
Borislav Mavrin, Hengshuai Yao, Linglong Kong   +2 more
openaire   +2 more sources

A survey of deep reinforcement learning technologies for intelligent air combat

Hangkong gongcheng jinzhan
Major aviation nations and related research institutions are focusing on exploration and research of key technologies for intelligent air combat. Deep reinforcement learning combines the perceptual ability of deep learning with the decision-making ...
LI Ni, LIAN Yunxiao, ZHOU Pan, XIE Feng, TANG Zhili, ZHOU Haoran, CHEN Jun   +6 more
doaj   +1 more source

Bayesian Deep Reinforcement Learning via Deep Kernel Learning

International Journal of Computational Intelligence Systems, 2018
Reinforcement learning (RL) aims to resolve the sequential decision-making under uncertainty problem where an agent needs to interact with an unknown environment with the expectation of optimising the cumulative long-term reward. Many real-world problems
Junyu Xuan, Jie Lu, Zheng Yan, Guangquan Zhang   +3 more
doaj   +1 more source

LFDC: Low-Energy Federated Deep Reinforcement Learning for Caching Mechanism in Cloud–Edge Collaborative

Applied Sciences, 2023
The optimization of caching mechanisms has long been a crucial research focus in cloud–edge collaborative environments. Effective caching strategies can substantially enhance user experience quality in these settings.
Xinyu Zhang, Zhigang Hu, Meiguang Zheng, Yang Liang, Hui Xiao, Hao Zheng, Aikun Xu   +6 more
doaj   +1 more source

Multiagent Deep Reinforcement Learning Algorithms in StarCraft II: A Review

IEEE Access
StarCraft II, as a real-time strategy game, features multiagent collaboration, complex decision-making processes, partially observable environments, and long-term credit assignment; thus, it is an ideal platform for exploring, validating, and optimizing ...
Yanyan Li, Yijun Wang, Yiwei Zhou
doaj   +1 more source

BoxStacker: Deep Reinforcement Learning for 3D Bin Packing Problem in Virtual Environment of Logistics Systems

Sensors, 2023
Manufacturing systems need to be resilient and self-organizing to adapt to unexpected disruptions, such as product changes or rapid order, in supply chain changes while increasing the automation level of robotized logistics processes to cope with the ...
Shokhikha Amalana Murdivien, Jumyung Um
doaj   +1 more source

Relational Deep Reinforcement Learning

CoRR, 2018
We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception and relational reasoning. It uses self-attention to iteratively reason about the relations between entities in a scene and to guide a model-free policy ...
Vinícius Flores Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li 0001, Igor Babuschkin, Karl Tuyls, David P. Reichert, Timothy P. Lillicrap, Edward Lockhart, Murray Shanahan, Victoria Langston, Razvan Pascanu, Matthew M. Botvinick, Oriol Vinyals, Peter W. Battaglia   +15 more
openaire   +2 more sources

A Comprehensive Review of Mobile Robot Navigation Using Deep Reinforcement Learning Algorithms in Crowded Environments

Journal of Intelligent and Robotic Systems
Navigation is a crucial challenge for mobile robots. Currently, deep reinforcement learning has attracted considerable attention and has witnessed substantial development owing to its robust performance and learning capabilities in real-world scenarios ...
Hoangcong Le, Saeed Saeedvand, Chen-Chien Hsu   +2 more
semanticscholar   +1 more source