Reinforcement Learning-Driven Innovation Clusters: Strategic Planning for Sustainable Corporate Growth
Cooperate Growth; Innovation clusters; ML; RL; Sustainable cooperation
This paper explores the role of reinforcement learning (RL) in optimizing innovation clusters to foster sustainable corporate growth. We go on to establish how RL allows organizations to optimize core performance metrics (innovation output, profit growth, sustainability impact and resource allocation efficiency), and show in dynamic datasets how a network of simulated strategic decisions were made in an innovation ecosystem. Moreover, it highlights the ability of RL to adapt to ever changing industries and implement long term strategic plans besides traditional strategic practices. The results demonstrate that RL-based methods contribute to unleashing innovation and profitabilising the companies, but also to more sustainable operations, bringing into proportion the growth and social responsibility. These results demonstrate RL as an implication tool with a strong future for optimizing corporate strategies that serves as an incentive for further innovation, translating into long-term viability and success. 2025 IEEE.
Jain, Yogesh Kumar; Pandey, Ankur; Jyothi, J.; Maheshwari, Abhishek
2025 IEEE 2nd International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2025;
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICITEICS64870.2025.11341550 https://www.scopus.com/pages/publications/105033222912?origin=resultslist
Jain Y.K., School of Management, IILM University, Uttar Pradesh, Greater Noida, India; Pandey A., Manipal University Jaipur, Department of Computer Science & Engineering, Jaipur, India; Jyothi J., Presidency College (Autonomous), Commerce and Management, Bengaluru, India; Maheshwari A., School of Commerce, Finance and Accountancy Christ (Deemed to Be) University, Ghaziabad, India