ISAR - International Journal of Research in Engineering Technology– Volume 8, Issue 2, September - October - 2023 ISSN: 2455 – 1341 www.IJORET.com Page 1 Optimizing Software AI Systems with Asynchronous Advantage Actor-Critic, Trust- Region Policy Optimization, and Learning in Partially Observable Markov Decision Processes Rahul_Jadon CarGurus Inc,Massachusetts,USA rahuljadon974@gmail.com Kannan Srinivasan, Saiana Technologies Inc, New Jersy, USA kannan.srini3108@gmail.com Guman_Singh_Chauhan, John Tesla Inc,California,USA gumanc38@gmail.com Rajababu_Budda IBM,California,USA RajBudda55@gmail.com Abstract Background Software systems based on AI, particularly those centered around reinforcement learning (RL), thrive in dynamic settings where data is incomplete. The integration of Asynchronous Advantage Actor-Critic (A3C), Trust-Region Policy Optimization (TRPO), and Partially Observable Markov Decision Processes (POMDPs) greatly enhances decision-making and resilience, especially in uncertain scenarios. Methods The combined method utilizes A3C’s asynchronous learning for quicker convergence, TRPO’s stability in updating policies, and POMDPs’ flexibility in situations with incomplete data, improving learning efficiency and decision-making quality in complicated AI settings. Objectives This research seeks to enhance decision-making in AI systems, evaluate the collaboration of A3C, TRPO, and POMDPs, and showcase improvements in their adaptability and stability. Uses encompass robotics, traffic management, and self-governing systems in environments that are partially observable. Result The suggested approach exceeded the performance of standalone methods in critical metrics, showing a 92% enhancement in decision-making efficiency and an 89% decrease in errors. It emphasizes a strong, flexible strategy appropriate for unpredictable, changing settings. Conclusion In summary, the findings highlight the significance of the research and its implications for future studies. The insights gained can inform practical applications and