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Transformers in reinforcement learning: a survey
Transformers have significantly impacted domains like natural language processing,
computer vision, and robotics, where they improve performance compared to other neural …
computer vision, and robotics, where they improve performance compared to other neural …
[HTML][HTML] Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
The integration of advanced image analysis using artificial intelligence (AI) is pivotal for the
evolution of autonomous vehicles (AVs). This article provides a thorough review of the most …
evolution of autonomous vehicles (AVs). This article provides a thorough review of the most …
Structure in deep reinforcement learning: A survey and open problems
Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …
Networks (DNNs) for function approximation, has demonstrated considerable success in …
Dynamics generalisation in reinforcement learning via adaptive context-aware policies
While reinforcement learning has achieved remarkable successes in several domains, its
real-world application is limited due to many methods failing to generalise to unfamiliar …
real-world application is limited due to many methods failing to generalise to unfamiliar …
Universal morphology control via contextual modulation
Learning a universal policy across different robot morphologies can significantly improve
learning efficiency and generalization in continuous control. However, it poses a challenging …
learning efficiency and generalization in continuous control. However, it poses a challenging …
The impact of task underspecification in evaluating deep reinforcement learning
Abstract Evaluations of Deep Reinforcement Learning (DRL) methods are an integral part of
scientific progress of the field. Beyond designing DRL methods for general intelligence …
scientific progress of the field. Beyond designing DRL methods for general intelligence …
Learning long-term crop management strategies with cyclesgym
To improve the sustainability and resilience of modern food systems, designing improved
crop management strategies is crucial. The increasing abundance of data on agricultural …
crop management strategies is crucial. The increasing abundance of data on agricultural …
[HTML][HTML] Contextual reinforcement learning for supply chain management
Efficient generalisation in supply chain inventory management is challenging due to a
potential mismatch between the model optimised and objective reality. It is hard to know how …
potential mismatch between the model optimised and objective reality. It is hard to know how …
Generalizing cooperative eco-driving via multi-residual task learning
Conventional control, such as model-based control, is commonly utilized in autonomous
driving due to its efficiency and reliability. However, real-world autonomous driving contends …
driving due to its efficiency and reliability. However, real-world autonomous driving contends …
Reinforcement learning pulses for transmon qubit entangling gates
The utility of a quantum computer is highly dependent on the ability to reliably perform
accurate quantum logic operations. For finding optimal control solutions, it is of particular …
accurate quantum logic operations. For finding optimal control solutions, it is of particular …