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Reinforcement learning for multi-agent with asynchronous missing information fusion method
Most current research on multi-agent reinforcement learning assumes a reliable environment where agents have globally accurate observations. However,...
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ARLO: An asynchronous update reinforcement learning-based offloading algorithm for mobile edge computing
The processing of large volumes of data sets unprecedented demands on the computing power of devices, and it is evident that resource-constrained...
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Digital twin-enabled adaptive scheduling strategy based on deep reinforcement learning
The modern complicated manufacturing industry and smart manufacturing tendency have imposed new requirements on the scheduling method, such as...
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Transfer Learning in Deep Reinforcement Learning
Reinforcement learning has quickly risen in popularity because of its simple, intuitive nature, and its powerful results. In this paper, we study a...
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Relabeling and policy distillation of hierarchical reinforcement learning
Hierarchical reinforcement learning (HRL) is a promising method to extend traditional reinforcement learning to solve more complex tasks. HRL can...
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A Procedural Constructive Learning Mechanism with Deep Reinforcement Learning for Cognitive Agents
Recent advancements in AI and deep learning have created a growing demand for artificial agents capable of performing tasks within increasingly...
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Optimization of Fed-Batch Baker’s Yeast Fermentation Using Deep Reinforcement Learning
Fermentation is widely used in chemical industries to produce valuable products. It consumes less energy and has a lesser environmental impact...
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Deep Reinforcement Learning with Heuristic Corrections for UGV Navigation
Mapless navigation for mobile Unmanned Ground Vehicles (UGVs) using Deep Reinforcement Learning (DRL) has attracted significantly rising attention in...
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Mapless navigation for UAVs via reinforcement learning from demonstrations
This paper is concerned with the problems of mapless navigation for unmanned aerial vehicles in the scenarios with limited sensor accuracy and...
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Reinforcement Learning: A Brief Overview
Learning techniques can be usefully grouped by the type of feedback that is available to the learner. A commonly drawn distinction is that between...
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Compensated Motion and Position Estimation of a Cable-driven Parallel Robot Based on Deep Reinforcement Learning
Unlike conventional rigid-link parallel robots, cable-driven parallel robots (CDPRs) have distinct advantages, including lower inertia, higher...
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Strategic Conflict Management using Recurrent Multi-agent Reinforcement Learning for Urban Air Mobility Operations Considering Uncertainties
The rapidly evolving urban air mobility (UAM) develops the heavy demand for public air transport tasks and poses great challenges to safe and...
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Track Learning Agent Using Multi-objective Reinforcement Learning
Reinforcement learning (RL) enables agents to make decisions through interactions with their environment and feedback in the form of rewards or...
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Transferring policy of deep reinforcement learning from simulation to reality for robotics
Deep reinforcement learning has achieved great success in many fields and has shown promise in learning robust skills for robot control in recent...
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Reinforcement Learning With Stereo-View Observation for Robust Electronic Component Robotic Insertion
In modern manufacturing, assembly tasks are a major challenge for robotics. In the manufacturing industry, a wide range of insertion tasks can be...
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Distributed Multi-agent Target Search and Tracking With Gaussian Process and Reinforcement Learning
Deploying multiple robots for target search and tracking has many practical applications, yet the challenge of planning over unknown or partially...
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Boosting in-transit entertainment: deep reinforcement learning for intelligent multimedia caching in bus networks
Multimedia content delivery in advanced networks faces exponential growth in data volumes, rendering existing solutions obsolete. This research...
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Reinforcement Learning-Based Energy Management for Hybrid Power Systems: State-of-the-Art Survey, Review, and Perspectives
The new energy vehicle plays a crucial role in green transportation, and the energy management strategy of hybrid power systems is essential for...
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Enhancing Mario Gaming Using Optimized Reinforcement Learning
“In the realm of classic gaming, Mario has held a special place in the hearts of players for generations. This study, titled ‘Enhancing Mario Gaming...
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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...
