US 12,001,320 B2
Cloud architecture for reinforcement learning
Xiaoyan Liu, Bothell, WA (US); Steve K. Lim, Redmond, WA (US); Taylor Paul Spangler, Kirkland, WA (US); Kashyap Maheshkumar Patel, Bellevue, WA (US); Marc Mas Mezquita, Barcelona (ES); Levent Ozgur, Seattle, WA (US); and Timothy James Chapman, Bellevue, WA (US)
Assigned to MICROSOFT TECHNOLOGY LICENSING, LLC, Redmond, WA (US)
Filed by MICROSOFT TECHNOLOGY LICENSING, LLC, Redmond, WA (US)
Filed on Jun. 30, 2022, as Appl. No. 17/855,434.
Claims priority of provisional application 63/341,791, filed on May 13, 2022.
Prior Publication US 2023/0367697 A1, Nov. 16, 2023
Int. Cl. G06F 9/44 (2018.01); G06F 11/36 (2006.01); G06N 3/08 (2023.01); G06N 3/10 (2006.01)
CPC G06F 11/3664 (2013.01) [G06F 11/368 (2013.01); G06F 11/3688 (2013.01); G06N 3/08 (2013.01); G06N 3/10 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method of automated software testing comprising:
selecting, by a reinforcement-learning model, a first action to be taken in a first user interface of a first software instance running in a first live environment;
causing the first action to be performed, wherein the first action includes interacting with a first interface element in the first user interface;
determine a first reward associated with the first action;
selecting, by the reinforcement-learning model, a second action to be taken in the first user interface of a second software instance running in a second live environment;
causing the second action to be performed, wherein the second action includes interacting with a second interface element in the first user interface;
determine a second reward associated with the second action;
generating an updated reinforcement-learning model by training the reinforcement-learning model using the first action, the second action, the first reward, and the second reward; and
storing the updated reinforcement-learning model.