Summary of Hadamard Representations: Augmenting Hyperbolic Tangents in Rl, by Jacob E. Kooi et al.
Hadamard Representations: Augmenting Hyperbolic Tangents in RL
by Jacob E. Kooi, Mark Hoogendoorn, Vincent François-Lavet
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 performance of activation functions in deep neural networks, particularly with respect to reinforcement learning. The authors highlight the strengths and limitations of continuously differentiable activations (e.g., tanh) and piece-wise linear functions (e.g., ReLU), which have been widely used in various applications. They provide insights into the vanishing gradients associated with continuously differentiable activations, which can lead to a dying neuron problem. To alleviate these issues, the authors propose a Hadamard representation that can be applied to various activation functions. The proposed approach is tested using deep Q-networks, proximal policy optimization, and parallelized Q-networks in the Atari domain, demonstrating faster learning, reduced dead neurons, and increased effective rank. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make artificial neural networks work better. Neural networks are a type of computer program that can learn and get smarter over time. The authors looked at different ways to make the networks “wake up” when they’re not working well. They found that some types of activation functions, which help neurons in the network talk to each other, don’t always work as well as others. To fix this problem, the authors came up with a new idea called Hadamard representation. They tested it and showed that it makes the networks learn faster and work better. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning » Relu » Tanh
