aiqbench analysis
This research validates the viability of spiking neural networks (SNNs) for continuous control tasks by demonstrating that a neuromorphic-friendly actor variant achieves performance nearly equivalent to traditional Soft Actor-Critic models on conventional hardware. By establishing this baseline, the study confirms that algorithmic efficiency in complex environments does not inherently require specialized hardware before realizing potential benefits from neuromorphic architectures. These findings provide a critical foundation for future work bridging the gap between current deep learning frameworks and next-generation neuromorphic computing systems.
Spiking Neural Networks for Continuous Control: Neuromorphic Reinforcement Learning in Conventional Computing
arXiv:2608.22729v1 Announce Type: new Abstract: Reinforcement learning (RL) algorithms have made strides over the past decade applying them to a wide range of problems and control tasks. However, the deployment of RL on neuromorphic hardware for continuous control tasks remains under-validated. Namely it is unclear whether replacing a conventional actor network with a spiking neural network (SNN) affects the performance of an agent before any hardware-specific benefits manifest. We provide a systematic validation of a minimal, neuromorphically viable spiking actor variant of Soft Actor-Critic (SAC) on conventional hardware, establishing a baseline for future neuromorphic RL research. In this paper, we propose the Spiking Actor Network Soft Actor Critic (SANSAC) to address the use of RL frameworks in continuous environments, designed as a framework that can be implemented on neuromorphic hardware. We compare a traditional Soft Actor Critic (SAC) network to SANSAC in a traditional computer. We demonstrate the near equivalent performance of SANSAC and SAC, while addressing the impact of hidden dimensions. Our results demonstrate the viability of SNN based algorithms in complex continuous environments, as well as competitive performance to traditional neural networks in traditional computers, providing a basis to continue exploring the use of SNNs in continuous RL frameworks.
Abstract:Reinforcement learning (RL) algorithms have made strides over the past decade applying them to a wide range of problems and control tasks. However, the deployment of RL on neuromorphic hardware for continuous control tasks remains under-validated. Namely it is unclear whether replacing a conventional actor network with a spiking neural network (SNN) affects the performance of an agent before any hardware-specific benefits manifest. We provide a systematic validation of a minimal, neuromorphically viable spiking actor variant of Soft Actor-Critic (SAC) on conventional hardware, establishing a baseline for future neuromorphic RL research. In this paper, we propose the Spiking Actor Network Soft Actor Critic (SANSAC) to address the use of RL frameworks in continuous environments, designed as a framework that can be implemented on neuromorphic hardware. We compare a traditional Soft Actor Critic (SAC) network to SANSAC in a traditional computer. We demonstrate the near equivalent performance of SANSAC and SAC, while addressing the impact of hidden dimensions. Our results demonstrate the viability of SNN based algorithms in complex continuous environments, as well as competitive performance to traditional neural networks in traditional computers, providing a basis to continue exploring the use of SNNs in continuous RL frameworks.
Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
arXiv:2608.22729 [cs.LG]
(or arXiv:2608.22729v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.22729 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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