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Reinforcement Learning Optimal Feedback Control with Industrial Applications
This book offers a thorough introduction to the basics and scientific and technological innovations involved in the modern study of...
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A reinforcement learning approach for thermostat setpoint preference learning
Occupant-centric controls (OCC) is an indoor climate control approach whereby occupant feedback is used in the sequence of operation of building...
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Maximum diffusion reinforcement learning
Robots and animals both experience the world through their bodies and senses. Their embodiment constrains their experiences, ensuring that they...
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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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State-Dependent Maximum Entropy Reinforcement Learning for Robot Long-Horizon Task Learning
Task-oriented robot learning has shown significant potential with the development of Reinforcement Learning (RL) algorithms. However, the learning of...
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Better value estimation in Q-learning-based multi-agent reinforcement learning
In many real-life scenarios, multiple agents necessitate cooperation to accomplish tasks. Benefiting from the significant success of deep learning,...
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Robot autonomous gras** and assembly skill learning based on deep reinforcement learning
This paper proposes a deep reinforcement learning-based framework for robot autonomous gras** and assembly skill learning. Meanwhile, a deep...
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Data-efficient model-based reinforcement learning with trajectory discrimination
Deep reinforcement learning has always been used to solve high-dimensional complex sequential decision problems. However, one of the biggest...
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Model-based deep reinforcement learning for accelerated learning from flow simulations
In recent years, deep reinforcement learning has emerged as a technique to solve closed-loop flow control problems. Employing simulation-based...
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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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Efficient relation extraction via quantum reinforcement learning
Most existing relation extraction methods only determine the relation type after identifying all entities, thus not fully modeling the interaction...
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Reinforcement Learning for Model Problems of Optimal Control
AbstractThe functionals of dynamic systems of various types are optimized using modern methods of reinforcement learning. The linear resource...
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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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Reinforcement Learning Navigation for Robots Based on Hippocampus Episode Cognition
Artificial intelligence is currently achieving impressive success in all fields. However, autonomous navigation remains a major challenge for AI....
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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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Fluid dynamic control and optimization using deep reinforcement learning
This paper presents a review of recent research on applying deep reinforcement learning in fluid dynamics. Reinforcement learning is a technique in...
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Quantum reinforcement learning via policy iteration
Quantum computing has shown the potential to substantially speed up machine learning applications, in particular for supervised and unsupervised...
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UAV Head-On Situation Maneuver Generation Using Transfer-Learning-Based Deep Reinforcement Learning
Recently, the demand for unmanned aerial vehicle technology has increased. In particular, AI pilots through reinforcement learning (RL) are more...
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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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VR Scene Detail Enhancement Method Based on Depth Reinforcement Learning Algorithm
In virtual reality, due to factors such as light sources and surface materials of objects, the details of the scene exhibit extremely complex...
