Learning To Control Dynamical Agents Via Spiking Neural Networks And Metropolis-hastings Sampling

Abstract

Spiking Neural Networks (SNNs) offer biologically inspired, energy-efficient alternatives to traditional Deep Neural Networks (DNNs) for real-time control systems. However, their training presents several challenges, particularly for reinforcement learning (RL) tasks, due to the non-differentiable nature of spike-based communication. In this work, we introduce what is, to our knowledge, the first framework that employs Metropolis-Hastings (MH) sampling, a Bayesian inference technique, to train SNNs for dynamical agent control in RL environments without relying on gradient-based methods. Our approach iteratively proposes and probabilistically accepts network parameter updates based on accumulated reward signals, effectively circumventing the limitations of backpropagation while enabling direct optimization on neuromorphic platforms. We evaluated this framework on two standard control benchmarks: AcroBot and CartPole. The results demonstrate that our MH-based approach outperforms convent

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keysafa2025learning

Related papers

• Deep Reinforcement Learning With Spiking Q-learning (2022)0.00
• Adaptive Surrogate Gradients For Sequential Reinforcement Learning In Spiking Neural Networks (2025)0.00
• Fully Spiking Actor Network With Intra-layer Connections For Reinforcement Learning (2024)0.00
• Learning First-to-spike Policies For Neuromorphic Control Using Policy Gradients (2018)8.60
• Deep Reinforcement Learning With Population-coded Spiking Neural Network For Continuous Control (2020)0.00
• Reinforcement Learning With A Network Of Spiking Agents (2019)0.00
• Scalable Multi-task Learning Through Spiking Neural Networks With Adaptive Task-switching Policy For Intelligent Autonomous Agents (2025)0.00
• Human-level Control Through Directly-trained Deep Spiking Q-networks (2021)12.40