Zobrazeno 1 - 10
of 890
pro vyhledávání: '"Farookh, A."'
Offline-to-online reinforcement learning (RL) leverages both pre-trained offline policies and online policies trained for downstream tasks, aiming to improve data efficiency and accelerate performance enhancement. An existing approach, Policy Expansi
Externí odkaz:
http://arxiv.org/abs/2410.23737
Autor:
Ahmadi, Sahar, Cheraghian, Ali, Saberi, Morteza, Abir, Md. Towsif, Dastmalchi, Hamidreza, Hussain, Farookh, Rahman, Shafin
Recent advances in deep learning for processing point clouds hold increased interest in Few-Shot Class Incremental Learning (FSCIL) for 3D computer vision. This paper introduces a new method to tackle the Few-Shot Continual Incremental Learning (FSCI
Externí odkaz:
http://arxiv.org/abs/2410.09237
Unsupervised pre-training has been on the lookout for the virtue of a value function representation referred to as successor features (SFs), which decouples the dynamics of the environment from the rewards. It has a significant impact on the process
Externí odkaz:
http://arxiv.org/abs/2405.02569
Most exploration research on reinforcement learning (RL) has paid attention to `the way of exploration', which is `how to explore'. The other exploration research, `when to explore', has not been the main focus of RL exploration research. The issue o
Externí odkaz:
http://arxiv.org/abs/2305.01322
Publikováno v:
International Journal of Production Research; Nov2024, Vol. 62 Issue 22, p8056-8072, 17p
Publikováno v:
In Internet of Things October 2024 27
Publikováno v:
In Future Generation Computer Systems October 2024 159:91-101
In this paper, we propose a recommendation framework named Cayley-Dickson Recommender. We introduce Cayley-Dickson construction which uses a recursive process to define hypercomplex algebras and their mathematical operations. We also design a graph c
Externí odkaz:
http://arxiv.org/abs/2112.08632
Publikováno v:
In Expert Systems With Applications 15 August 2024 248
The high-dimensional or sparse reward task of a reinforcement learning (RL) environment requires a superior potential controller such as hierarchical reinforcement learning (HRL) rather than an atomic RL because it absorbs the complexity of commands
Externí odkaz:
http://arxiv.org/abs/2107.08183