no code implementations • 4 Feb 2024 • Arsalan SharifNassab, Saber Salehkaleybar, Richard Sutton
This paper addresses the challenge of optimizing meta-parameters (i. e., hyperparameters) in machine learning algorithms, a critical factor influencing training efficiency and model performance.
no code implementations • 30 Jan 2024 • Thomas Degris, Khurram Javed, Arsalan SharifNassab, Yuxin Liu, Richard Sutton
We conclude by suggesting that combining both approaches could be a promising future direction to improve the performance of neural networks in continual learning.
no code implementations • 31 Jan 2023 • Arsalan SharifNassab, Richard Sutton
Gradient-based methods for value estimation in reinforcement learning have favorable stability properties, but they are typically much slower than Temporal Difference (TD) learning methods.
no code implementations • 25 Oct 2022 • Banafsheh Rafiee, Sina Ghiassian, Jun Jin, Richard Sutton, Jun Luo, Adam White
In this paper, we explore an approach to auxiliary task discovery in reinforcement learning based on ideas from representation learning.
1 code implementation • 9 Nov 2020 • Banafsheh Rafiee, Zaheer Abbas, Sina Ghiassian, Raksha Kumaraswamy, Richard Sutton, Elliot Ludvig, Adam White
We present three new diagnostic prediction problems inspired by classical-conditioning experiments to facilitate research in online prediction learning.
2 code implementations • ICLR 2020 • Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, Hado van Hasselt
bsuite is a collection of carefully-designed experiments that investigate core capabilities of reinforcement learning (RL) agents with two objectives.