  This paper introduces AutoGCN, a generic Neural Architecture Search (NAS)
algorithm for Human Activity Recognition (HAR) using Graph Convolution Networks
(GCNs). HAR has gained attention due to advances in deep learning, increased
data availability, and enhanced computational capabilities. At the same time,
GCNs have shown promising results in modeling relationships between body key
points in a skeletal graph. While domain experts often craft dataset-specific
GCN-based methods, their applicability beyond this specific context is severely
limited. AutoGCN seeks to address this limitation by simultaneously searching
for the ideal hyperparameters and architecture combination within a versatile
search space using a reinforcement controller while balancing optimal
exploration and exploitation behavior with a knowledge reservoir during the
search process. We conduct extensive experiments on two large-scale datasets
focused on skeleton-based action recognition to assess the proposed algorithm's
performance. Our experimental results underscore the effectiveness of AutoGCN
in constructing optimal GCN architectures for HAR, outperforming conventional
NAS and GCN methods, as well as random search. These findings highlight the
significance of a diverse search space and an expressive input representation
to enhance the network performance and generalizability.
