Kinetic parameters estimated with dynamic 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) help characterize hepatocellular carcinoma (HCC), and deep reinforcement learning (DRL) can improve kinetic parameter estimation.The advantage actor-critic (A2C) algorithm is a DRL algorithm with neural networks that seek the optimal parameters. The aim of this study was to preliminarily assess the role of the A2C algorithm in estimating the kinetic parameters of 18F-FDG PET/CT in patients with HCC.18F-FDG PET data from 14 liver tissues and 17 HCC tumors obtained via a previously developed, abbreviated acquisition protocol (5-min dynamic PET/CT imaging supplemented with 1-min static imaging at 60 min) were prospectively collected. The A2C algorithm was used to estimate kinetic parameters with a reversible double-input, three-compartment model, and the results were compared with those of the conventional nonlinear least squares (NLLS) algorithm. Fitting errors were compared via the root-mean-square errors (RMSEs) of the time activity curves (TACs).Significant differences in K1, k2, k3, k4, fa, and vb according to the A2C algorithm and k3, fa, and vb according to the NLLS algorithm were detected between HCC and normal liver tissues (all p < 0.05). Furthermore, A2C demonstrated superior diagnostic performance over NLLS in terms of k3 and vb (both p < 0.05 in the Delong test). Notably, A2C yielded a smaller fitting error for normal liver tissue (0.62 ± 0.24 vs. 1.04 ± 1.00) and HCC tissue (1.40 ± 0.42 vs. 1.51 ± 0.97) than did NLLS.Compared with the conventional postreconstruction NLLS method, the A2C algorithm can more precisely estimate 18F-FDG kinetic parameters with a reversible double-input, three-compartment model for HCC tumors, attaining better TAC fitting with a lower RMSE.© 2025 American Association of Physicists in Medicine.