Zobrazeno 1 - 10
of 746
pro vyhledávání: '"Jackson, Matthew P."'
Autor:
Ye, Teng, Yan, Hanson, Huang, Xuhuan, Grogan, Connor, Yuan, Walter, Mei, Qiaozhu, Jackson, Matthew O.
With the rise of social media and peer-to-peer networks, users increasingly rely on crowdsourced responses for information and assistance. However, the mechanisms used to rank and promote responses often prioritize and end up biasing in favor of time
Externí odkaz:
http://arxiv.org/abs/2411.05328
Publikováno v:
2024 IEEE 7th International Conference on Soft Robotics (RoboSoft), San Diego, CA, USA, 2024, pp. 933-939
Soft robotic manipulators offer operational advantage due to their compliant and deformable structures. However, their inherently nonlinear dynamics presents substantial challenges. Traditional analytical methods often depend on simplifying assumptio
Externí odkaz:
http://arxiv.org/abs/2410.18519
Autor:
Goldie, Alexander David, Lu, Chris, Jackson, Matthew Thomas, Whiteson, Shimon, Foerster, Jakob Nicolaus
While reinforcement learning (RL) holds great potential for decision making in the real world, it suffers from a number of unique difficulties which often need specific consideration. In particular: it is highly non-stationary; suffers from high degr
Externí odkaz:
http://arxiv.org/abs/2407.07082
Autor:
Silva, Vinicius L S, Regnier, Geraldine, Salinas, Pablo, Heaney, Claire E, Jackson, Matthew D, Pain, Christopher C
Reactive transport in porous media plays a pivotal role in subsurface reservoir processes, influencing fluid properties and geochemical characteristics. However, coupling fluid flow and transport with geochemical reactions is computationally intensiv
Externí odkaz:
http://arxiv.org/abs/2405.14548
Autor:
Eiras, Francisco, Petrov, Aleksandar, Vidgen, Bertie, Schroeder, Christian, Pizzati, Fabio, Elkins, Katherine, Mukhopadhyay, Supratik, Bibi, Adel, Purewal, Aaron, Botos, Csaba, Steibel, Fabro, Keshtkar, Fazel, Barez, Fazl, Smith, Genevieve, Guadagni, Gianluca, Chun, Jon, Cabot, Jordi, Imperial, Joseph, Nolazco, Juan Arturo, Landay, Lori, Jackson, Matthew, Torr, Phillip H. S., Darrell, Trevor, Lee, Yong, Foerster, Jakob
Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about the potential risks of the tec
Externí odkaz:
http://arxiv.org/abs/2405.08597
Autor:
Eiras, Francisco, Petrov, Aleksandar, Vidgen, Bertie, de Witt, Christian Schroeder, Pizzati, Fabio, Elkins, Katherine, Mukhopadhyay, Supratik, Bibi, Adel, Csaba, Botos, Steibel, Fabro, Barez, Fazl, Smith, Genevieve, Guadagni, Gianluca, Chun, Jon, Cabot, Jordi, Imperial, Joseph Marvin, Nolazco-Flores, Juan A., Landay, Lori, Jackson, Matthew, Röttger, Paul, Torr, Philip H. S., Darrell, Trevor, Lee, Yong Suk, Foerster, Jakob
In the next few years, applications of Generative AI are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about potential risks
Externí odkaz:
http://arxiv.org/abs/2404.17047
Autor:
Jackson, Matthew Thomas, Matthews, Michael Tryfan, Lu, Cong, Ellis, Benjamin, Whiteson, Shimon, Foerster, Jakob
In many real-world settings, agents must learn from an offline dataset gathered by some prior behavior policy. Such a setting naturally leads to distribution shift between the behavior policy and the target policy being trained - requiring policy con
Externí odkaz:
http://arxiv.org/abs/2404.06356
A core ambition of reinforcement learning (RL) is the creation of agents capable of rapid learning in novel tasks. Meta-RL aims to achieve this by directly learning such agents. Black box methods do so by training off-the-shelf sequence models end-to
Externí odkaz:
http://arxiv.org/abs/2403.03020
Autor:
Matthews, Michael, Beukman, Michael, Ellis, Benjamin, Samvelyan, Mikayel, Jackson, Matthew, Coward, Samuel, Foerster, Jakob
Benchmarks play a crucial role in the development and analysis of reinforcement learning (RL) algorithms. We identify that existing benchmarks used for research into open-ended learning fall into one of two categories. Either they are too slow for me
Externí odkaz:
http://arxiv.org/abs/2402.16801
Autor:
Jackson, Matthew Thomas, Lu, Chris, Kirsch, Louis, Lange, Robert Tjarko, Whiteson, Shimon, Foerster, Jakob Nicolaus
Recent advancements in meta-learning have enabled the automatic discovery of novel reinforcement learning algorithms parameterized by surrogate objective functions. To improve upon manually designed algorithms, the parameterization of this learned ob
Externí odkaz:
http://arxiv.org/abs/2402.05828