Added missing URL to GammelliHarrisonEtAl2023.

_bibliography/ASL_Bib.bib

@@ -4229,7 +4229,8 @@ @InProceedings{GammelliHarrisonEtAl2023
   month     = jul,
   abstract  = {Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems. However, (1) traditional optimization-based approaches do not scale to large networks, and (2) the design of good heuristics or approximation algorithms often requires significant manual trial-and-error. In this work, we argue that data-driven strategies can automate this process and learn efficient algorithms without compromising optimality. To do so, we present network control problems through the lens of reinforcement learning and propose a graph network-based framework to handle a broad class of problems. Instead of naively computing actions over high-dimensional graph elements, e.g., edges, we propose a bi-level formulation where we (1) specify a desired next state via RL, and (2) solve a convex program to best achieve it, leading to drastically improved scalability and performance. We further highlight a collection of desirable features to system designers, investigate design decisions, and present experiments on real-world control problems showing the utility, scalability, and flexibility of our framework.},
   owner     = {jthluke},
-  timestamp = {2024-09-19},
+  timestamp = {2024-09-20},
+  url       = {https://arxiv.org/abs/2305.09129},
 }
 
 @inproceedings{GammelliHarrisonEtAl2022,

_bibliography/ASL_Bib.bib.bak

@@ -4230,6 +4230,7 @@
   abstract  = {Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems. However, (1) traditional optimization-based approaches do not scale to large networks, and (2) the design of good heuristics or approximation algorithms often requires significant manual trial-and-error. In this work, we argue that data-driven strategies can automate this process and learn efficient algorithms without compromising optimality. To do so, we present network control problems through the lens of reinforcement learning and propose a graph network-based framework to handle a broad class of problems. Instead of naively computing actions over high-dimensional graph elements, e.g., edges, we propose a bi-level formulation where we (1) specify a desired next state via RL, and (2) solve a convex program to best achieve it, leading to drastically improved scalability and performance. We further highlight a collection of desirable features to system designers, investigate design decisions, and present experiments on real-world control problems showing the utility, scalability, and flexibility of our framework.},
   owner     = {jthluke},
   timestamp = {2024-09-19},
+  url       = {https://arxiv.org/abs/2305.09129},
 }
 
 @inproceedings{GammelliHarrisonEtAl2022,
@@ -5071,7 +5072,7 @@
   timestamp = {2024-09-19}
 }
 
-@Article{BourdillonEtAl2023,
+@Article{BourdillonEtAl2022,
   author    = {Bourdillon, A. and Garg, A. and Wang, H. and Woo, Y. and Pavone, M. and Boyd, J.},
   title     = {Integration of Reinforcement Learning in a Virtual Robotic Surgical Simulation},
   journal   = jrn_SAGE_SI,
@@ -5180,7 +5181,7 @@ Conclusion. Game engines hold promising potential for the design and implementat
   url       = {https://arxiv.org/abs/2403.04057}
 }
 
-@InProceedings{BanerjeeSharmaEtAl2023,
+@InProceedings{BanerjeeSharmaEtAl2022,
   author    = {Banerjee, S. and Sharma, A. and Schmerling, E. and Spolaor, M. and Nemerouf, M. and Pavone, M.},
   title     = {Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications},
   booktitle = proc_IEEE_AC,