Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Ye, Weirui"'
Defining reward functions for skill learning has been a long-standing challenge in robotics. Recently, vision-language models (VLMs) have shown promise in defining reward signals for teaching robots manipulation skills. However, existing works often
Externí odkaz:
http://arxiv.org/abs/2405.13573
Sample efficiency remains a crucial challenge in applying Reinforcement Learning (RL) to real-world tasks. While recent algorithms have made significant strides in improving sample efficiency, none have achieved consistently superior performance acro
Externí odkaz:
http://arxiv.org/abs/2403.00564
Autor:
Ye, Weirui, Zhang, Yunsheng, Weng, Haoyang, Gu, Xianfan, Wang, Shengjie, Zhang, Tong, Wang, Mengchen, Abbeel, Pieter, Gao, Yang
Reinforcement learning (RL) is a promising approach for solving robotic manipulation tasks. However, it is challenging to apply the RL algorithms directly in the real world. For one thing, RL is data-intensive and typically requires millions of inter
Externí odkaz:
http://arxiv.org/abs/2310.02635
Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals. Therefore, text-conditioned video prediction (TVP) is an essential task to facilitate general robot policy learning. To tackle this task
Externí odkaz:
http://arxiv.org/abs/2303.14897
Autor:
Liu, Shaohuai, Liu, Jinbo, Ye, Weirui, Yang, Nan, Zhang, Guanglun, Zhong, Haiwang, Kang, Chongqing, Jiang, Qirong, Song, Xuri, Di, Fangchun, Gao, Yang
The growing renewable energy sources have posed significant challenges to traditional power scheduling. It is difficult for operators to obtain accurate day-ahead forecasts of renewable generation, thereby requiring the future scheduling system to ma
Externí odkaz:
http://arxiv.org/abs/2303.05205
Publikováno v:
Published at NeurIPS 2022
One of the most important AI research questions is to trade off computation versus performance since ``perfect rationality" exists in theory but is impossible to achieve in practice. Recently, Monte-Carlo tree search (MCTS) has attracted considerable
Externí odkaz:
http://arxiv.org/abs/2210.12628
Imitation learning is a class of promising policy learning algorithms that is free from many practical issues with reinforcement learning, such as the reward design issue and the exploration hardness. However, the current imitation algorithm struggle
Externí odkaz:
http://arxiv.org/abs/2210.09598
Reinforcement learning has achieved great success in many applications. However, sample efficiency remains a key challenge, with prominent methods requiring millions (or even billions) of environment steps to train. Recently, there has been significa
Externí odkaz:
http://arxiv.org/abs/2111.00210
Publikováno v:
In Journal of Energy Storage April 2021 36
