Zobrazeno 1 - 10
of 110
pro vyhledávání: '"Boulet, Benoît"'
Offline reinforcement learning (RL) enables policy learning from pre-collected offline datasets, relaxing the need to interact directly with the environment. However, limited by the quality of offline datasets, it generally fails to learn well-qualif
Externí odkaz:
http://arxiv.org/abs/2412.20519
Reward specification is one of the most tricky problems in Reinforcement Learning, which usually requires tedious hand engineering in practice. One promising approach to tackle this challenge is to adopt existing expert video demonstrations for polic
Externí odkaz:
http://arxiv.org/abs/2410.21795
Publikováno v:
27th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2024)
Trajectory prediction module in an autonomous driving system is crucial for the decision-making and safety of the autonomous agent car and its surroundings. This work presents a novel scheme called AiGem (Agent-Interaction Graph Embedding) to predict
Externí odkaz:
http://arxiv.org/abs/2410.23298
The hype around self-driving cars has been growing over the past years and has sparked much research. Several modules in self-driving cars are thoroughly investigated to ensure safety, comfort, and efficiency, among which the controller is crucial. T
Externí odkaz:
http://arxiv.org/abs/2410.11979
In this work, we investigate how to leverage pre-trained visual-language models (VLM) for online Reinforcement Learning (RL). In particular, we focus on sparse reward tasks with pre-defined textual task descriptions. We first identify the problem of
Externí odkaz:
http://arxiv.org/abs/2406.00645
The autonomous driving industry is expected to grow by over 20 times in the coming decade and, thus, motivate researchers to delve into it. The primary focus of their research is to ensure safety, comfort, and efficiency. An autonomous vehicle has se
Externí odkaz:
http://arxiv.org/abs/2404.12256
Efficient traffic signal control is critical for reducing traffic congestion and improving overall transportation efficiency. The dynamic nature of traffic flow has prompted researchers to explore Reinforcement Learning (RL) for traffic signal contro
Externí odkaz:
http://arxiv.org/abs/2312.07795
Hacking and false data injection from adversaries can threaten power grids' everyday operations and cause significant economic loss. Anomaly detection in power grids aims to detect and discriminate anomalies caused by cyber attacks against the power
Externí odkaz:
http://arxiv.org/abs/2303.06431
Safety has been recognized as the central obstacle to preventing the use of reinforcement learning (RL) for real-world applications. Different methods have been developed to deal with safety concerns in RL. However, learning reliable RL-based solutio
Externí odkaz:
http://arxiv.org/abs/2302.03586
MetaEMS: A Meta Reinforcement Learning-based Control Framework for Building Energy Management System
The building sector has been recognized as one of the primary sectors for worldwide energy consumption. Improving the energy efficiency of the building sector can help reduce the operation cost and reduce the greenhouse gas emission. The energy manag
Externí odkaz:
http://arxiv.org/abs/2210.12590