We propose and demonstrate experimentally and numerically a network of three globally coupled semiconductor lasers (SLs) that generate triple-channel chaotic signals with time delayed signature (TDS) concealment. The effects of the coupling strength and bias current on the concealment of the TDS are investigated. The generated chaotic signals are further applied to reinforcement learning, and a parallel scheme is proposed to solve the multiarmed bandit (MAB) problem. The influences of mutual correlation between signals from different channels, the sampling interval of signals, and the TDS concealment on the performance of decision making are analyzed. Comparisons between the proposed scheme and two existing schemes show that, with a simplified algorithm, the proposed scheme can perform as well as the previous schemes or even better. Moreover, we also consider the robustness of decision making performance against a dynamically changing environment and verify the scalability for MAB problems with different sizes. This proposed globally coupled SL network for a multi-channel chaotic source is simple in structure and easy to implement. The attempt to solve the MAB problem in parallel can provide potential values in the realm of the application of ultrafast photonics intelligence.