Spiking Neural Network Actor-Critic Proximal Policy Optimization Control for Autonomous UAV Navigation Through Constrained Openings in Civil Infrastructure and Buildings

The paper presents a spiking neural network-based Proximal Policy Optimization (SNN‑PPO) algorithm for autonomous UAV navigation through constrained openings in civil infrastructure. By integrating spike‑based actor‑critic reinforcement learning with a stochastic Gaussian policy, the method achieved 63.77 % overall success across 3000+ episodes, improving to over 90 % in later stages and averaging 2.10 windows per episode.

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• agents
• reinforcement-learning
• robotics