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Plasticity of Growing and Elastic Neural Networks in Online Continual Learning
Jeong Min Kong, Richard S. Sutton
Neural networks that can grow or both grow and shrink during learning, referred to as growing neural networks and elastic neural networks, respectively, have recently been explored…
Intentional Updates for Streaming Reinforcement Learning
Arsalan Sharifnassab, Mohamed Elsayed, Kris De Asis +2
In gradient-based learning, a step size chosen in parameter units does not produce a predictable per-step change in function output. This often leads to instability in the streamin…
Swift-Sarsa: Fast and Robust Linear Control
Khurram Javed, Richard S. Sutton
Javed, Sharifnassab, and Sutton (2024) introduced a new algorithm for TD learning -- SwiftTD -- that augments True Online TD() with step-size optimization, a bound on the effect…
MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters
Arsalan Sharifnassab, Saber Salehkaleybar, Richard Sutton
We address the challenge of optimizing meta-parameters (hyperparameters) in machine learning, a key factor for efficient training and high model performance. Rather than relying on…
Step-size Optimization for Continual Learning
Thomas Degris, Khurram Javed, Arsalan Sharifnassab +2
In continual learning, a learner has to keep learning from the data over its whole life time. A key issue is to decide what knowledge to keep and what knowledge to let go. In a neu…