Search
Search Results
-
Deep reinforcement learning applied to an assembly sequence planning problem with user preferences
Deep reinforcement learning (DRL) has demonstrated its potential in solving complex manufacturing decision-making problems, especially in a context...
-
An efficient planning method based on deep reinforcement learning with hybrid actions for autonomous driving on highway
Due to the complexity and uncertainty of the traffic, planning for autonomous driving (AD) on highway is challenging. Traditional planning algorithms...
-
Deep Model-Based Reinforcement Learning for Predictive Control of Robotic Systems with Dense and Sparse Rewards
Sparse rewards and sample efficiency are open areas of research in the field of reinforcement learning. These problems are especially important when...
-
Unstructured surface mesh smoothing method based on deep reinforcement learning
In numerical simulations such as computational fluid dynamics simulations or finite element analyses, mesh quality affects simulation accuracy...
-
Intelligent deep reinforcement learning-based scheduling in relay-based HetNets
We consider a fundamental file dissemination problem in a two-hop relay-based heterogeneous network consisting of a macro base station, a half-duplex...
-
Utilizing deep reinforcement learning for tactile-based autonomous capture of non-cooperative objects in space
The focus of this research is the creation of a deep reinforcement learning approach to tackle the challenging task of robotic grip** through...
-
Mutated Deep Reinforcement Learning Scheduling in Cloud for Resource-Intensive IoT Systems
Cloud computing has indisputably emerged as the primary computing and storage platform for various contemporary workloads. These workloads, spanning...
-
Deep Reinforcement Learning for Heat Pump Control
Heating in private households is a major contributor to the emissions generated today. Heat pumps are a promising alternative for heat generation and...
-
Robot Autonomous Avoidance System Based on Reinforcement Learning in 6G Network Scenarios
Robotic platforms must combine autonomous avoidance systems to operate safely and effectively, particularly in uncertain and insecure situations....
-
Application Study on the Reinforcement Learning Strategies in the Network Awareness Risk Perception and Prevention
The intricacy of wireless network ecosystems and Internet of Things (IoT) connected devices have increased rapidly as technology advances and cyber...
-
Designing an Interpretability Analysis Framework for Deep Reinforcement Learning (DRL) Agents in Highway Automated Driving Simulation
Explainability is a key requirement for users to effectively understand, trust, and manage artificial intelligence applications, especially those...
-
Axis-space framework for cable-driven soft continuum robot control via reinforcement learning
Cable-driven soft continuum robots are important tools in minimally invasive surgery (MIS) to reduce the lesions, pain and risk of infection. The...
-
Consistent epistemic planning for multiagent deep reinforcement learning
Multiagent cooperation in a partially observable environment without communication is difficult because of the uncertainty of agents. Traditional...
-
A General Unbiased Training Framework for Deep Reinforcement Learning
Deep reinforcement learning (DRL) combines deep neural networks with reinforcement learning which enables agents to learn the best actions in virtual...
-
A Deep Reinforcement Learning Approach for Production Scheduling with the Use of Dispatch Rules
This work proposes a framework of a Deep Reinforcement Learning (DRL) scheduling agent that solves the production scheduling problem by deciding the...
-
Reward Function Design Method for Long Episode Pursuit Tasks Under Polar Coordinate in Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning has recently been applied to solve pursuit problems. However, it suffers from a large number of time steps per...
-
Autonomous imaging scheduling networks of small celestial bodies flyby based on deep reinforcement learning
During the flyby mission of small celestial bodies in deep space, it is hard for spacecraft to take photos at proper positions only rely on...
-
A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis
Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained...
-
A deep reinforcement learning model for resilient road network recovery under earthquake or flooding hazards
As the backbone and the ‘blood vessel’ of modern cities, road networks provide critical support for community activities and economic growth, with...
-
DSPVR: dynamic SFC placement with VNF reuse in Fog-Cloud Computing using Deep Reinforcement Learning
The advent of Network Function Virtualization (NFV) has enabled the flexible provisioning of services on Fog-Cloud Computing-based Networks (CFCN)...
