Zobrazeno 1 - 10
of 2 472
pro vyhledávání: '"Sullivan Ryan"'
Autor:
Sullivan, Ryan, Pégoud, Ryan, Rahmen, Ameen Ur, Yang, Xinchen, Huang, Junyun, Verma, Aayush, Mitra, Nistha, Dickerson, John P.
Curriculum learning has been a quiet yet crucial component of many of the high-profile successes of reinforcement learning. Despite this, none of the major reinforcement learning libraries directly support curriculum learning or include curriculum le
Externí odkaz:
http://arxiv.org/abs/2411.11318
Autor:
Wang, Kaiwen, Kidambi, Rahul, Sullivan, Ryan, Agarwal, Alekh, Dann, Christoph, Michi, Andrea, Gelmi, Marco, Li, Yunxuan, Gupta, Raghav, Dubey, Avinava, Ramé, Alexandre, Ferret, Johan, Cideron, Geoffrey, Hou, Le, Yu, Hongkun, Ahmed, Amr, Mehta, Aranyak, Hussenot, Léonard, Bachem, Olivier, Leurent, Edouard
Reward-based finetuning is crucial for aligning language policies with intended behaviors (e.g., creativity and safety). A key challenge is to develop steerable language models that trade-off multiple (conflicting) objectives in a flexible and effici
Externí odkaz:
http://arxiv.org/abs/2407.15762
We present Meta MMO, a collection of many-agent minigames for use as a reinforcement learning benchmark. Meta MMO is built on top of Neural MMO, a massively multiagent environment that has been the subject of two previous NeurIPS competitions. Our wo
Externí odkaz:
http://arxiv.org/abs/2406.05071
Autor:
Sullivan, Ryan P., Morningstar, John T., Castellanos, Eduardo, Bradford III, Robert W., Hofstetter, Yvonne J., Vaynzof, Yana, Welker, Mark E., Jurchescu, Oana D.
Molecular-scale diodes made from self-assembled monolayers (SAMs) could complement silicon-based technologies with smaller, cheaper, and more versatile devices. However, advancement of this emerging technology is limited by insufficient electronic pe
Externí odkaz:
http://arxiv.org/abs/2404.11261
Autor:
Wang, Dongang, Liu, Peilin, Wang, Hengrui, Beadnall, Heidi, Kyle, Kain, Ly, Linda, Cabezas, Mariano, Zhan, Geng, Sullivan, Ryan, Cai, Weidong, Ouyang, Wanli, Calamante, Fernando, Barnett, Michael, Wang, Chenyu
Training deep neural networks reliably requires access to large-scale datasets. However, obtaining such datasets can be challenging, especially in the context of neuroimaging analysis tasks, where the cost associated with image acquisition and annota
Externí odkaz:
http://arxiv.org/abs/2404.03451
Autor:
Huang, Shengyi, Gallouédec, Quentin, Felten, Florian, Raffin, Antonin, Dossa, Rousslan Fernand Julien, Zhao, Yanxiao, Sullivan, Ryan, Makoviychuk, Viktor, Makoviichuk, Denys, Danesh, Mohamad H., Roumégous, Cyril, Weng, Jiayi, Chen, Chufan, Rahman, Md Masudur, Araújo, João G. M., Quan, Guorui, Tan, Daniel, Klein, Timo, Charakorn, Rujikorn, Towers, Mark, Berthelot, Yann, Mehta, Kinal, Chakraborty, Dipam, KG, Arjun, Charraut, Valentin, Ye, Chang, Liu, Zichen, Alegre, Lucas N., Nikulin, Alexander, Hu, Xiao, Liu, Tianlin, Choi, Jongwook, Yi, Brent
In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms. However, the complete raw data of the learning curves are rarely available. As a result, it is usually necessary to repro
Externí odkaz:
http://arxiv.org/abs/2402.03046
We introduce a novel policy learning method that integrates analytical gradients from differentiable environments with the Proximal Policy Optimization (PPO) algorithm. To incorporate analytical gradients into the PPO framework, we introduce the conc
Externí odkaz:
http://arxiv.org/abs/2312.08710
Autor:
Suárez, Joseph, Isola, Phillip, Choe, Kyoung Whan, Bloomin, David, Li, Hao Xiang, Pinnaparaju, Nikhil, Kanna, Nishaanth, Scott, Daniel, Sullivan, Ryan, Shuman, Rose S., de Alcântara, Lucas, Bradley, Herbie, Castricato, Louis, You, Kirsty, Jiang, Yuhao, Li, Qimai, Chen, Jiaxin, Zhu, Xiaolong
Neural MMO 2.0 is a massively multi-agent environment for reinforcement learning research. The key feature of this new version is a flexible task system that allows users to define a broad range of objectives and reward signals. We challenge research
Externí odkaz:
http://arxiv.org/abs/2311.03736
Most reinforcement learning methods rely heavily on dense, well-normalized environment rewards. DreamerV3 recently introduced a model-based method with a number of tricks that mitigate these limitations, achieving state-of-the-art on a wide range of
Externí odkaz:
http://arxiv.org/abs/2310.17805
Autor:
Bai, Lei, Wang, Dongang, Barnett, Michael, Cabezas, Mariano, Cai, Weidong, Calamante, Fernando, Kyle, Kain, Liu, Dongnan, Ly, Linda, Nguyen, Aria, Shieh, Chun-Chien, Sullivan, Ryan, Wang, Hengrui, Zhan, Geng, Ouyang, Wanli, Wang, Chenyu
Accurately measuring the evolution of Multiple Sclerosis (MS) with magnetic resonance imaging (MRI) critically informs understanding of disease progression and helps to direct therapeutic strategy. Deep learning models have shown promise for automati
Externí odkaz:
http://arxiv.org/abs/2308.16376