Summary of Normalization and Effective Learning Rates in Reinforcement Learning, by Clare Lyle and Zeyu Zheng and Khimya Khetarpal and James Martens and Hado Van Hasselt and Razvan Pascanu and Will Dabney
Normalization and effective learning rates in reinforcement learning
by Clare Lyle, Zeyu Zheng, Khimya Khetarpal, James Martens, Hado van Hasselt, Razvan Pascanu, Will Dabney
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Normalization layers have recently gained attention in deep reinforcement learning and continual learning, offering benefits like improved loss landscape conditioning and combatting overestimation bias. However, this also introduces a side effect: an equivalence between growing network parameter norms and decaying effective learning rates. This becomes problematic in continual learning settings, where the effective learning rate may decay too quickly relative to the learning problem’s timescale. To address this, we propose Normalize-and-Project (NaP), which couples normalization layers with weight projection to maintain a constant effective learning rate throughout training. NaP is both an analytical tool for understanding learning rate schedules and a means of improving robustness to nonstationarity in synthetic plasticity loss benchmarks, demonstrated through experiments on single-task and sequential variants of the Arcade Learning Environment. Our approach can be applied to popular architectures like ResNets and transformers, recovering or even slightly improving performance in common stationary benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A recent discovery in artificial intelligence has shown that a new technique called normalization layers can help with learning and remembering new things. This is good because it can make the learning process better and more stable. However, this also means that the learning rate might slow down too quickly, which can be bad. To solve this problem, we came up with an idea called Normalize-and-Project (NaP). It helps keep the learning rate steady while still making sure the network is learning properly. We tested NaP on some popular artificial intelligence models and found that it works well and even improves their performance in certain situations. |
Keywords
* Artificial intelligence * Attention * Continual learning * Reinforcement learning
