Summary of Frequency and Generalisation Of Periodic Activation Functions in Reinforcement Learning, by Augustine N. Mavor-parker et al.
Frequency and Generalisation of Periodic Activation Functions in Reinforcement Learning
by Augustine N. Mavor-Parker, Matthew J. Sargent, Caswell Barry, Lewis Griffin, Clare Lyle
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 This paper investigates the impact of periodic activation functions on deep reinforcement learning (RL) algorithms. Periodic activations have been shown to improve sample efficiency and stability in various RL methods. Two hypotheses have been proposed to explain these improvements: one suggesting that periodic activations learn low-frequency representations, avoiding overfitting, while another proposes they learn high-frequency representations, allowing networks to quickly fit complex value functions. Empirical analysis reveals that periodic representations consistently converge to high frequencies regardless of initial frequency and that periodic activation functions improve sample efficiency but exhibit worse generalization when observing noisy states. Additionally, weight decay regularization can partially offset the overfitting of periodic activation functions, leading to value functions that learn quickly while generalizing well. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how different types of “activation functions” affect deep learning algorithms for decision-making. Some people thought these new functions improved performance because they helped avoid mistakes or learned complex patterns. The researchers tested this idea and found out what really happens. They discovered that the activation functions always tend to learn high-frequency patterns, no matter where they start. These new functions do improve how quickly an algorithm can learn, but it’s not as good at handling unexpected situations. Finally, adding a special kind of “regularization” helps prevent overfitting and makes the algorithm more reliable. |
Keywords
* Artificial intelligence * Deep learning * Generalization * Overfitting * Regularization * Reinforcement learning
