Evolving-to-learn Reinforcement Learning Tasks With Spiking Neural Networks

Abstract

Inspired by the natural nervous system, synaptic plasticity rules are applied to train spiking neural networks with local information, making them suitable for online learning on neuromorphic hardware. However, when such rules are implemented to learn different new tasks, they usually require a significant amount of work on task-dependent fine-tuning. This paper aims to make this process easier by employing an evolutionary algorithm that evolves suitable synaptic plasticity rules for the task at hand. More specifically, we provide a set of various local signals, a set of mathematical operators, and a global reward signal, after which a Cartesian genetic programming process finds an optimal learning rule from these components. Using this approach, we find learning rules that successfully solve an XOR and cart-pole task, and discover new learning rules that outperform the baseline rules from literature.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keylu2022evolving

Related papers

• Hebbian Synaptic Modifications In Spiking Neurons That Learn (2019)0.00
• Reinforcement Learning With A Network Of Spiking Agents (2019)0.00
• Neuroevolution Of Recurrent Architectures On Control Tasks (2023)4.52
• Adaptive Surrogate Gradients For Sequential Reinforcement Learning In Spiking Neural Networks (2025)0.00
• Deep Reinforcement Learning With Spiking Q-learning (2022)0.00
• Learning Fast Changing Slow In Spiking Neural Networks (2024)3.58
• Noise-based Reward-modulated Learning (2025)0.00
• Learning First-to-spike Policies For Neuromorphic Control Using Policy Gradients (2018)8.60