Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games

Patel, Devdhar, Hazan, Hananel, Saunders, Daniel J., Siegelmann, Hava, Kozma, Robert Machine Learning 

Various implementations of Deep Reinforcement Learning (RL) demonstrated excellent performance on tasks that can be solved by trained policy, but they are not without drawbacks. Deep RL suffers from high sensitivity to noisy and missing input and adversarial attacks. To mitigate these deficiencies of deep RL solutions, we suggest involving spiking neural networks (SNNs). Previous work has shown that standard Neural Networks trained using supervised learning for image classification can be converted to SNNs with negligible deterioration in performance. In this paper, we convert Q-Learning ReLU-Networks (ReLU-N) trained using reinforcement learning into SNN. We provide a proof of concept for the conversion of ReLU-N to SNN demonstrating improved robustness to occlusion and better generalization than the original ReLU-N. Moreover, we show promising initial results with converting full-scale Deep Q-networks to SNNs, paving the way for future research.