Search Results - "Farebrother, Jesse"

CALE: Continuous Arcade Learning Environment by Farebrother, Jesse, Castro, Pablo Samuel

“…We introduce the Continuous Arcade Learning Environment (CALE), an extension of the well-known Arcade Learning Environment (ALE) [Bellemare et al., 2013]. The…”
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Foundations of Multivariate Distributional Reinforcement Learning by Wiltzer, Harley, Farebrother, Jesse, Gretton, Arthur, Rowland, Mark

“…In reinforcement learning (RL), the consideration of multivariate reward signals has led to fundamental advancements in multi-objective decision-making,…”
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Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching by Jain, Arnav Kumar, Wiltzer, Harley, Farebrother, Jesse, Rish, Irina, Berseth, Glen, Choudhury, Sanjiban

“…In inverse reinforcement learning (IRL), an agent seeks to replicate expert demonstrations through interactions with the environment. Traditionally, IRL is…”
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A Distributional Analogue to the Successor Representation by 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…”
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A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces by 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,…”
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Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks by 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…”
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Stop Regressing: Training Value Functions via Classification for Scalable Deep RL by 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…”
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Mixtures of Experts Unlock Parameter Scaling for Deep RL by 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…”
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Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy by Schwarzer, Max, Farebrother, Jesse, Greaves, Joshua, Cubuk, Ekin Dogus, Agarwal, Rishabh, Courville, Aaron, Bellemare, Marc G, Kalinin, Sergei, Mordatch, Igor, Castro, Pablo Samuel, Roccapriore, Kevin M

“…We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimulated by the…”
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Generalization and Regularization in DQN by Farebrother, Jesse, Machado, Marlos C, Bowling, Michael

“…Deep reinforcement learning algorithms have shown an impressive ability to learn complex control policies in high-dimensional tasks. However, despite the…”
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