Hadamard Representations: Augmenting Hyperbolic Tangents in RL

• URL: http://arxiv.org/abs/2406.09079v2
• Date: Wed, 23 Oct 2024 08:05:57 GMT
• Title: Hadamard Representations: Augmenting Hyperbolic Tangents in RL
• Authors: Jacob E. Kooi, Mark Hoogendoorn, Vincent François-Lavet,
• Abstract summary: Activation functions are one of the key components of a deep neural network. In reinforcement learning, the performance of continuously differentiable activations often falls short as compared to linear-unit functions. We show faster learning, a reduction in dead neurons and increased effective rank using deep Q-networks.
• Score: 8.647614188619315
• License:
• Abstract: Activation functions are one of the key components of a deep neural network. The most commonly used activation functions can be classed into the category of continuously differentiable (e.g. tanh) and linear-unit functions (e.g. ReLU), both having their own strengths and drawbacks with respect to downstream performance and representation capacity through learning (e.g. measured by the number of dead neurons and the effective rank). In reinforcement learning, the performance of continuously differentiable activations often falls short as compared to linear-unit functions. We provide insights into the vanishing gradients associated with the former, and show that the dying neuron problem is not exclusive to ReLU's. To alleviate vanishing gradients and the resulting dying neuron problem occurring with continuously differentiable activations, we propose a Hadamard representation. Using deep Q-networks and proximal policy optimization in the Atari domain, we show faster learning, a reduction in dead neurons and increased effective rank.

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