Zobrazeno 1 - 10
of 198
pro vyhledávání: '"Albrecht, Stefano V"'
Meta-Reinforcement Learning (Meta-RL) agents can struggle to operate across tasks with varying environmental features that require different optimal skills (i.e., different modes of behaviours). Using context encoders based on contrastive learning to
Externí odkaz:
http://arxiv.org/abs/2406.04815
Reinforcement Learning (RL) algorithms often suffer from low training efficiency. A strategy to mitigate this issue is to incorporate a model-based planning algorithm, such as Monte Carlo Tree Search (MCTS) or Value Iteration (VI), into the environme
Externí odkaz:
http://arxiv.org/abs/2405.11727
The rapidly changing architecture and functionality of electrical networks and the increasing penetration of renewable and distributed energy resources have resulted in various technological and managerial challenges. These have rendered traditional
Externí odkaz:
http://arxiv.org/abs/2404.15583
Large language models (LLMs) have shown significant potential for robotics applications, particularly task planning, by harnessing their language comprehension and text generation capabilities. However, in applications such as household robotics, a c
Externí odkaz:
http://arxiv.org/abs/2404.14285
Autor:
Dunion, Mhairi, Albrecht, Stefano V.
The performance of image-based Reinforcement Learning (RL) agents can vary depending on the position of the camera used to capture the images. Training on multiple cameras simultaneously, including a first-person egocentric camera, can leverage infor
Externí odkaz:
http://arxiv.org/abs/2404.14064
Autor:
Gyevnar, Balint, Droop, Stephanie, Quillien, Tadeg, Cohen, Shay B., Bramley, Neil R., Lucas, Christopher G., Albrecht, Stefano V.
Cognitive science can help us understand which explanations people might expect, and in which format they frame these explanations, whether causal, counterfactual, or teleological (i.e., purpose-oriented). Understanding the relevance of these concept
Externí odkaz:
http://arxiv.org/abs/2403.08828
Artificial Intelligence (AI) shows promising applications for the perception and planning tasks in autonomous driving (AD) due to its superior performance compared to conventional methods. However, inscrutable AI systems exacerbate the existing chall
Externí odkaz:
http://arxiv.org/abs/2402.10086
Autonomous agents trained using deep reinforcement learning (RL) often lack the ability to successfully generalise to new environments, even when these environments share characteristics with the ones they have encountered during training. In this wo
Externí odkaz:
http://arxiv.org/abs/2402.03479
Although much research has been done on proposing new models or loss functions to improve the generalisation of artificial neural networks (ANNs), less attention has been directed to the impact of the training data on generalisation. In this work, we
Externí odkaz:
http://arxiv.org/abs/2401.08808
Despite numerous successes, the field of reinforcement learning (RL) remains far from matching the impressive generalisation power of human behaviour learning. One possible way to help bridge this gap be to provide RL agents with richer, more human-l
Externí odkaz:
http://arxiv.org/abs/2312.04736