Learning to Act through Activation Function Optimization in Random Networks

Joachim Winther Pedersen, Sebastian Risi

Keywords: artificial neural networks, activation functions, neural diversity

Abstract:

Biological neural networks are characterised by a high degree of neural diversity, a trait that artificial neural networks (ANNs) generally lack.
Additionally, learning in ANNs is typically synonymous with only modifying the strengths of connection weights.
However, there is much evidence from neuroscience that different classes of neurons each have crucial roles in the information processing done by the network. In nature, each neuron is a dynamical system that is a powerful information processor in its own right. In this paper we ask the question, how well can ANNs learn to perform reinforcement learning tasks only through the optimization of neural activation functions, without any weight optimization?
We demonstrate the viability of the method and show that the neural parameters are expressive enough to allow learning three different continuous control tasks without weight optimization.
These results open up for more possibilities for synergies between synaptic and neural optimization in ANNs in the future.

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TL;DR: We optimize parameterized activation functions in fixed random networks to solve reinforcement learning tasks.