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Active Exploration Deep Reinforcement Learning for Continuous Action Space with Forward Prediction
The application of reinforcement learning (RL) to the field of autonomous robotics has high requirements about sample efficiency, since the agent...
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Robust reinforcement learning with UUB guarantee for safe motion control of autonomous robots
This paper addresses the issue of safety in reinforcement learning (RL) with disturbances and its application in the safety-constrained motion...
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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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ARSL-V: A risk-aware relay selection scheme using reinforcement learning in VANETs
In high-speed and dynamic Vehicular Ad-hoc Networks (VANETs), cooperative transmission mechanism is a promising scheme to ensure the sustainable...
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Reinforcement learning-based multi-objective energy-efficient task scheduling in fog-cloud industrial IoT-based systems
The advancement of Industrial Internet of Things (IIoT) applications has increased the demand for efficient and energy-aware task scheduling in...
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IoT Network with Energy Efficiency for Dynamic Sink via Reinforcement Learning
In a society where better, cleaner power generation and management are needed, IoT devices and battery technologies have gained prominence. The...
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Dynamic warning zone and a short-distance goal for autonomous robot navigation using deep reinforcement learning
Robot navigation in crowded environments has recently benefited from advances in deep reinforcement learning (DRL) approaches. However, it still...
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Blocklength Allocation and Power Control in UAV-Assisted URLLC System via Multi-agent Deep Reinforcement Learning
Integration of unmanned aerial vehicles (UAVs) with ultra-reliable and low-latency communication (URLLC) systems can improve the real-time...
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Exoatmospheric Evasion Guidance Law with Total Energy Limit via Constrained Reinforcement Learning
Due to the lack of aerodynamic forces, the available propulsion for exoatmospheric pursuit-evasion problem is strictly limited, which has not been...
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Guiding real-world reinforcement learning for in-contact manipulation tasks with Shared Control Templates
The requirement for a high number of training episodes has been a major limiting factor for the application of Reinforcement Learning (RL) in...
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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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An Adaptive Model-Free Control Method for Metro Train Based on Deep Reinforcement Learning
The current metro train control system has achieved automatic operation, but the degree of intelligence needs to be enhanced. To improve the...
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Application of Reinforcement Learning to Dyeing Processes for Residual Dye Reduction
Sustainability has become a prominent theme in the manufacturing industry, with an emphasis on optimal process configurations that enable...
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Multi-objective Deep Reinforcement Learning Based Joint Beamforming and Power Allocation in UAV Assisted Cellular Communication
In order to provide spectrum and energy efficient communication for unmanned aerial vehicle assisted cellular network, the problem of joint...
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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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Preference-based experience sharing scheme for multi-agent reinforcement learning in multi-target environments
Multi-agent reinforcement learning is a varied and highly active field of research. The idea of parameter sharing or experience sharing has recently...
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A path planning method based on deep reinforcement learning for crowd evacuation
Deep reinforcement learning (DRL) is suitable for solving complex path-planning problems due to its excellent ability to make continuous decisions in...
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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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Dynamic link utilization empowered by reinforcement learning for adaptive storage allocation in MANET
In modern wireless networks, mobile nodes often deal with the challenge of maintaining a sufficient number of data packets due to limited storage...
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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...