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pro vyhledávání: '"Farebrother, Jesse"'
In reinforcement learning (RL), the consideration of multivariate reward signals has led to fundamental advancements in multi-objective decision-making, transfer learning, and representation learning. This work introduces the first oracle-free and co
Externí odkaz:
http://arxiv.org/abs/2409.00328
Autor:
Farebrother, Jesse, Orbay, Jordi, Vuong, Quan, Taïga, Adrien Ali, Chebotar, Yevgen, Xiao, Ted, Irpan, Alex, Levine, Sergey, Castro, Pablo Samuel, Faust, Aleksandra, Kumar, Aviral, Agarwal, Rishabh
Value functions are a central component of deep reinforcement learning (RL). These functions, parameterized by neural networks, are trained using a mean squared error regression objective to match bootstrapped target values. However, scaling value-ba
Externí odkaz:
http://arxiv.org/abs/2403.03950
Autor:
Obando-Ceron, Johan, Sokar, Ghada, Willi, Timon, Lyle, Clare, Farebrother, Jesse, Foerster, Jakob, Dziugaite, Gintare Karolina, Precup, Doina, Castro, Pablo Samuel
The recent rapid progress in (self) supervised learning models is in large part predicted by empirical scaling laws: a model's performance scales proportionally to its size. Analogous scaling laws remain elusive for reinforcement learning domains, ho
Externí odkaz:
http://arxiv.org/abs/2402.08609
Autor:
Wiltzer, Harley, Farebrother, Jesse, Gretton, Arthur, Tang, Yunhao, Barreto, André, Dabney, Will, Bellemare, Marc G., Rowland, Mark
This paper contributes a new approach for distributional reinforcement learning which elucidates a clean separation of transition structure and reward in the learning process. Analogous to how the successor representation (SR) describes the expected
Externí odkaz:
http://arxiv.org/abs/2402.08530
Autor:
Farebrother, Jesse, Greaves, Joshua, Agarwal, Rishabh, Lan, Charline Le, Goroshin, Ross, Castro, Pablo Samuel, Bellemare, Marc G.
Auxiliary tasks improve the representations learned by deep reinforcement learning agents. Analytically, their effect is reasonably well understood; in practice, however, their primary use remains in support of a main learning objective, rather than
Externí odkaz:
http://arxiv.org/abs/2304.12567
Autor:
Lan, Charline Le, Greaves, Joshua, Farebrother, Jesse, Rowland, Mark, Pedregosa, Fabian, Agarwal, Rishabh, Bellemare, Marc G.
Many machine learning problems encode their data as a matrix with a possibly very large number of rows and columns. In several applications like neuroscience, image compression or deep reinforcement learning, the principal subspace of such a matrix p
Externí odkaz:
http://arxiv.org/abs/2212.04025
Deep reinforcement learning algorithms have shown an impressive ability to learn complex control policies in high-dimensional tasks. However, despite the ever-increasing performance on popular benchmarks, policies learned by deep reinforcement learni
Externí odkaz:
http://arxiv.org/abs/1810.00123
Autor:
Roccapriore, Kevin M, Schwarzer, Max, Greaves, Joshua, Farebrother, Jesse, Torsi, Riccardo, Agarwal, Rishabh, Bishop, Colton, Mordatch, Igor, Cubuk, Ekin D, Courville, Aaron, Bellemare, Marc G, Robinson, Joshua, Castro, Pablo Samuel, Kalinin, Sergei V
Publikováno v:
Microscopy & Microanalysis; 2024 Supplement, Vol. 30, p1-4, 4p
Autor:
Goldthorpe, Zach, Cannon, Jason, Farebrother, Jesse, Friggstad, Zachary, Nascimento, Mario A.
Publikováno v:
21st SIGSPATIAL International Conference on Advances in Geographic Information Systems; 11/6/2018, p630-633, 4p
Autor:
Roccapriore, Kevin M, Schwarzer, Max, Greaves, Joshua, Farebrother, Jesse, Agarwal, Rishabh, Bishop, Colton, Ziatdinov, Maxim, Mordatch, Igor, Cubuk, Ekin D, Courville, Aaron, Castro, Pablo Samuel, Bellemare, Marc G, Kalinin, Sergei V
Publikováno v:
Microscopy & Microanalysis; 2023 Supplement, p1932-1933, 2p