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SpikeAEC: a neuromodulation-based spiking controller for explore-exploit balancing in mobile robots.

Canyang Liu¹, Yichen Liu¹, Yongqi Zhou²

¹School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China.

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|March 23, 2026

Summary

This summary is machine-generated.

We introduce SpikeAEC, a novel brain-inspired AI architecture for mobile robots that balances exploration and exploitation. This system enhances control by learning faster and achieving better performance than existing methods.

Keywords:

actor-explorer-critic exploration-exploitation dilemma neuromodulation spiking neural networks (SNNs)three-factor learning rules

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Area of Science:

Computational Neuroscience
Robotics
Artificial Intelligence

Background:

Mobile robot control faces challenges in balancing exploration and exploitation, often leading to suboptimal performance.
Existing policies may converge on inefficient behaviors, hindering reliable operation.
Brain-inspired adaptive control offers a promising avenue for overcoming these limitations.

Purpose of the Study:

To propose SpikeAEC, a fully spiking, neuromodulated Actor-Explorer-Critic architecture for online, closed-loop mobile robot control.
To address the exploration-exploitation dilemma using a biologically plausible computational framework.
To enhance policy refinement and action selection in robotic systems.

Main Methods:

Developed SpikeAEC, integrating Actor (basal ganglia), Explorer (ACC-GPe-STN pathway), and Critic (ventral striatum) subnetworks.
Employed a three-factor learning framework with temporal-difference (TD) error and neuromodulators (acetylcholine, dopamine) for synaptic plasticity.
Utilized a TAN-based Arbitrator for probabilistic action selection based on performance and TD error.

Main Results:

SpikeAEC demonstrated superior performance in simulations compared to leading brain-inspired methods.
Achieved 24% faster convergence, 18% reduction in trajectory length, and over 5% increase in cumulative reward.
Maintained consistency with neurophysiological principles throughout the study.

Conclusions:

SpikeAEC effectively balances exploration and exploitation in mobile robot control.
The neuromodulated, spiking architecture facilitates online policy refinement and adaptive behavior.
This approach offers a robust and biologically plausible solution for enhancing robotic control systems.