Update README.md

Added a reference in the main README.md to the article by Pope et al. 2023

README.md

@@ -47,7 +47,9 @@ JSBSim is used in a range of projects among which:
 
 JSBSim is also used in academic and industry research ([more than 700 citations referenced by Google Scholar](https://scholar.google.com/scholar?&q=jsbsim) as of May 2023).
 
-In 2023 JSBSim has been featured in the article ["A deep reinforcement learning control approach for high-performance aircraft"](https://link.springer.com/article/10.1007/s11071-023-08725-y) on _Nonlinear Dynamics_, an International Journal of Nonlinear Dynamics and Chaos in Engineering Systems by Springer. The open access article is available as a PDF here [https://link.springer.com/content/pdf/10.1007/s11071-023-08725-y.pdf](https://link.springer.com/content/pdf/10.1007/s11071-023-08725-y.pdf). The work demonstrates an application of Deep Reinforcement Learning (DRL) to flight control and guidance, leveraging the JSBSim interface to MATLAB/Simulink.
+In 2023 JSBSim was featured in the article ["A deep reinforcement learning control approach for high-performance aircraft"](https://link.springer.com/article/10.1007/s11071-023-08725-y) , by De Marco et al. (2023), _Nonlinear Dynamics_, an International Journal of Nonlinear Dynamics and Chaos in Engineering Systems by Springer (doi: 10.1007/s11071-023-08725-y). The open-access article is available as a PDF here [https://link.springer.com/content/pdf/10.1007/s11071-023-08725-y.pdf](https://link.springer.com/content/pdf/10.1007/s11071-023-08725-y.pdf). The work demonstrates an application of Deep Reinforcement Learning (DRL) to flight control and guidance, leveraging the JSBSim interface to MATLAB/Simulink.
+
+Another more advanced application within the field of Deep Reinforcement Learning is presented in the article ["Hierarchical Reinforcement Learning for Air Combat at DARPA's AlphaDogfight Trials"](https://ieeexplore.ieee.org/document/9950612) by A. P. Pope et al. (2023), _IEEE Transactions on Artificial Intelligence_ (doi: 10.1109/TAI.2022.3222143), featuring a hierarchical reinforcement learning approach. The trained agent was designed alongside of and competed against active fighter pilots, and ultimately defeated a graduate of the United States Air Force's F-16 Weapons Instructor Course in match play. See also the [DARPA Virtual Air Combat Competition](https://www.darpa.mil/news-events/2019-10-21).
 
 # User Guide