Rajmeet Singh Bhourji
Papers
1
Total Citations
31
H-Index
1
About
Rajmeet Singh Bhourji is a researcher at the forefront of intelligent control systems, specializing in reinforcement learning (RL) and its application to complex dynamical systems. His work bridges the gap between advanced machine learning algorithms and real-world control challenges, with a particular focus on stabilization and optimization problems. Bhourji’s most-cited paper, “Reinforcement Learning DDPG–PPO Agent-Based Control System for Rotary Inverted Pendulum” (2023, 31 citations), demonstrates a novel hybrid approach combining Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) agents. This work not only showcases the power of model-free RL in handling nonlinear, underactuated systems but also provides a benchmark for comparing algorithm performance in control tasks. By integrating these agents, Bhourji advances the practical deployment of RL in robotics and automation, offering a robust framework for real-time decision-making. His contributions are shaping the next generation of adaptive controllers, with clear implications for autonomous systems, industrial robotics, and smart manufacturing. With growing recognition in the control and AI communities, Bhourji’s research continues to inspire students and engineers exploring the synergy between deep learning and classical control theory.
Research Focus
Key Achievements
Top Papers
- 1