Problem
Research questions and friction points this paper is trying to address.
Spiking Neural Network
Predicting Sequence
External World States
Spiking Neural Network Predicting Sequence of the External Worlds States in Model-Based Reinforcement Learning
🤖 AI Summary
本文提出了一种脉冲神经网络，用于从当前状态开始预测外部世界状态序列，通过训练预测下一个状态的脉冲神经网络实现，并在ATARI乒乓球环境中测试。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
spiking neural network
sequence prediction
model-free reinforcement learning
neuronal ensemble
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