Intentional Updates for Streaming Reinforcement Learning
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 streaming setting (i.e., batch size=1), where stochasticity is not averaged out and update magnitudes can momentarily become arbitrarily big or small. Instead, we propose intentional updates: first specify the intended outcome of an update and then solve for the step size that approximately achieves it. This strategy has precedent in online supervised linear regression via Normalized Least Mean Squares algorithm, which selects a step size to yield a specified change in the function output proportional to the current err...
| Authors | Arsalan Sharifnassab, Mohamed Elsayed, Kris De Asis et al. |
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| Primary category | cs.LG |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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