Multi-task reinforcement learning with attention-based mixture of experts

Turnitin 降AI改写 早检测系统 早降重系统 Turnitin-UK版 万方检测-期刊版 维普编辑部版 Grammarly检测 Paperpass检测 checkpass检测 PaperYY检测
[Free GPT-4]
[DeepSeek]
[PDF] mlr.press

PreCo: Enhancing Generalization in Co-Design of Modular Soft Robots via Brain-Body Pre-Training

Y Wang, S Wu, T Zhang, Y Chang… - … on Robot Learning, 2023 - proceedings.mlr.press
Brain-body co-design, which involves the collaborative design of control strategies and
morphologies, has emerged as a promising approach to enhance a robot's adaptability to its …
Enregistrer Citer Cité 6 fois Autres articles Les 2 versionsFree GPT-4 DeepSeek Version HTML

Reinforcement learning with adaptive policy gradient transfer across heterogeneous problems

G Zhang, L Feng, Y Wang, M Li, H **
Z Huang, H Yuan, Y Fu, Z Lu - ar** across diverse objects presents a fundamental yet formidable
challenge in robot learning. Existing approaches using reinforcement learning (RL) to …
Enregistrer Citer Autres articles Les 3 versionsFree GPT-4 DeepSeek Version HTML
[Free GPT-4]
[DeepSeek]
[PDF] arxiv.org

SMOSE: Sparse Mixture of Shallow Experts for Interpretable Reinforcement Learning in Continuous Control Tasks

M Vincze, L Ferrarotti, LL Custode, B Lepri… - ar** is effective in addressing the sparse-reward challenge in reinforcement
learning by providing immediate feedback through auxiliary informative rewards. Based on …
Enregistrer Citer Autres articles Les 3 versionsFree GPT-4 DeepSeek Version HTML