Zobrazeno 1 - 10
of 1 635
pro vyhledávání: '"Denoyer, A"'
In dynamic domains such as autonomous robotics and video game simulations, agents must continuously adapt to new tasks while retaining previously acquired skills. This ongoing process, known as Continual Reinforcement Learning, presents significant c
Externí odkaz:
http://arxiv.org/abs/2412.14865
Offline Reinforcement Learning (RL) has emerged as a powerful alternative to imitation learning for behavior modeling in various domains, particularly in complex navigation tasks. An existing challenge with Offline RL is the signal-to-noise ratio, i.
Externí odkaz:
http://arxiv.org/abs/2411.07760
Developing agents for complex and underspecified tasks, where no clear objective exists, remains challenging but offers many opportunities. This is especially true in video games, where simulated players (bots) need to play realistically, and there i
Externí odkaz:
http://arxiv.org/abs/2411.01894
Standard cooperative multi-agent reinforcement learning (MARL) methods aim to find the optimal team cooperative policy to complete a task. However there may exist multiple different ways of cooperating, which usually are very needed by domain experts
Externí odkaz:
http://arxiv.org/abs/2308.14308
Deep reinforcement learning (DRL) techniques have become increasingly used in various fields for decision-making processes. However, a challenge that often arises is the trade-off between both the computational efficiency of the decision-making proce
Externí odkaz:
http://arxiv.org/abs/2308.09629
Autor:
Xiaodan Zhang, Tianhong Cheng, Ellie Cho, Wenjia Lu, Delphine Denoyer, Paul McMillan, Kalyan Shobhana, Swati Varshney, Nicholas A. Williamson, Alastair Stewart
Publikováno v:
Pharmacological Research, Vol 210, Iss , Pp 107519- (2024)
Many drugs have been discontinued during phase II/III breast cancer clinical trials due to lack of clinical efficacy, indicating shortcomings in predictive value of preclinical data. Nutrient availability in the tumour cell microenvironment and the d
Externí odkaz:
https://doaj.org/article/e44fe74df1654e5bada7fdbc506d06f2
Autor:
Mihai Dragos Maliia, Elif Köksal-Ersöz, Adrien Benard, Tristan Calas, Anca Nica, Yves Denoyer, Maxime Yochum, Fabrice Wendling, Pascal Benquet
Publikováno v:
Harvard Data Science Review (2024)
Externí odkaz:
https://doaj.org/article/58e9d26bef664552b9fcf17dc2860106
Autor:
Gaya, Jean-Baptiste, Doan, Thang, Caccia, Lucas, Soulier, Laure, Denoyer, Ludovic, Raileanu, Roberta
The ability to continuously acquire new knowledge and skills is crucial for autonomous agents. Existing methods are typically based on either fixed-size models that struggle to learn a large number of diverse behaviors, or growing-size models that sc
Externí odkaz:
http://arxiv.org/abs/2211.10445
Existing imitation learning methods mainly focus on making an agent effectively mimic a demonstrated behavior, but do not address the potential contradiction between the behavior style and the objective of a task. There is a general lack of efficient
Externí odkaz:
http://arxiv.org/abs/2209.13224
When users initiate search sessions, their queries are often unclear or might lack of context; this resulting in inefficient document ranking. Multiple approaches have been proposed by the Information Retrieval community to add context and retrieve d
Externí odkaz:
http://arxiv.org/abs/2205.15918