Spiking Neural Network Predicting Sequence of the External Worlds States in Model-Based Reinforcement Learning

Mikhail Kiselev

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arXiv · 2609.27459

Spiking Neural Network Predicting Sequence of the External Worlds States in Model-Based Reinforcement Learning

Mikhail Kiselev

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Abstract

This paper presents a spiking neural network (SNN) designed to predict the sequence of the external world states starting from the current world state. This SNN does not create the world dynamics model - instead it incorporates the SNN trained to predict the next world state and provides all mechanisms necessary to make the chain of predicted world states. These mechanisms are entirely spiking - they are implemented as spiking neuron ensembles. The present article describes this neuronal structure and tests its operation on a classic RL benchmark - ATARI ping-pong.

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BibTeX RIS

Mikhail Kiselev. 2026-09-23. Spiking Neural Network Predicting Sequence of the External Worlds States in Model-Based Reinforcement Learning. https://arxiv.org/abs/2609.27459

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