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pro vyhledávání: '"Bowling A"'
We study how a Reinforcement Learning (RL) system can remain sample-efficient when learning from an imperfect model of the environment. This is particularly challenging when the learning system is resource-constrained and in continual settings, where
Externí odkaz:
http://arxiv.org/abs/2406.19561
Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward
Externí odkaz:
http://arxiv.org/abs/2406.13909
Publikováno v:
International Journal of COPD, Vol Volume 14, Pp 461-470 (2019)
Jill A Ohar,1 Alyssa Bowling,2 Thomas Goodin,2 Barry Price,2 Ayca Ozol-Godfrey,2 Sanjay Sharma,2 Shahin Sanjar2 1Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA; 2Sunovion Pharmaceuticals Inc, Marlborough, MA,
Externí odkaz:
https://doaj.org/article/37420eaf31d24de69b0cf0716e9fb8f0
Autor:
Wiggins, Alessa Ibrahim, Bovill, Mia Sauda, Strolger, Louis-Gregory, Stiavelli, Massimo, Bowling, Cora
Population III (Pop III) stars formed out of metal free gas in minihalos at $z>20$. While their ignition ended the Dark Ages and begin enrichment of the IGM, their mass distribution remains unconstrained. To date, no confirmed Pop III star has been o
Externí odkaz:
http://arxiv.org/abs/2402.17076
Autor:
Parisi, Simone, Mohammedalamen, Montaser, Kazemipour, Alireza, Taylor, Matthew E., Bowling, Michael
In reinforcement learning (RL), an agent learns to perform a task by interacting with an environment and receiving feedback (a numerical reward) for its actions. However, the assumption that rewards are always observable is often not applicable in re
Externí odkaz:
http://arxiv.org/abs/2402.06819
Although the synthesis of programs encoding policies often carries the promise of interpretability, systematic evaluations were never performed to assess the interpretability of these policies, likely because of the complexity of such an evaluation.
Externí odkaz:
http://arxiv.org/abs/2311.06979
Autor:
Mehta, Deval, Betz-Stablein, Brigid, Nguyen, Toan D, Gal, Yaniv, Bowling, Adrian, Haskett, Martin, Sashindranath, Maithili, Bonnington, Paul, Mar, Victoria, Soyer, H Peter, Ge, Zongyuan
The surge in developing deep learning models for diagnosing skin lesions through image analysis is notable, yet their clinical black faces challenges. Current dermatology AI models have limitations: limited number of possible diagnostic outputs, lack
Externí odkaz:
http://arxiv.org/abs/2311.01009
Autor:
Wang, Zhe, Veličković, Petar, Hennes, Daniel, Tomašev, Nenad, Prince, Laurel, Kaisers, Michael, Bachrach, Yoram, Elie, Romuald, Wenliang, Li Kevin, Piccinini, Federico, Spearman, William, Graham, Ian, Connor, Jerome, Yang, Yi, Recasens, Adrià, Khan, Mina, Beauguerlange, Nathalie, Sprechmann, Pablo, Moreno, Pol, Heess, Nicolas, Bowling, Michael, Hassabis, Demis, Tuyls, Karl
Identifying key patterns of tactics implemented by rival teams, and developing effective responses, lies at the heart of modern football. However, doing so algorithmically remains an open research challenge. To address this unmet need, we propose Tac
Externí odkaz:
http://arxiv.org/abs/2310.10553
The ability to learn good representations of states is essential for solving large reinforcement learning problems, where exploration, generalization, and transfer are particularly challenging. The Laplacian representation is a promising approach to
Externí odkaz:
http://arxiv.org/abs/2310.10833
Autor:
Sychrovský, David, Šustr, Michal, Davoodi, Elnaz, Bowling, Michael, Lanctot, Marc, Schmid, Martin
The literature on game-theoretic equilibrium finding predominantly focuses on single games or their repeated play. Nevertheless, numerous real-world scenarios feature playing a game sampled from a distribution of similar, but not identical games, suc
Externí odkaz:
http://arxiv.org/abs/2303.01074