V. V. Moiseenko

Peoples' Friendship University of Russia

Papers

Total Citations

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About

V. V. Moiseenko is a researcher at the intersection of robotics, control theory, and artificial intelligence, with a primary focus on applying reinforcement learning to complex control problems. His most cited work, "Reinforcement Learning for Solving Control Problems in Robotics" (2023), introduces an innovative approach that combines reinforcement learning with evolutionary algorithms to find optimal control strategies. This method enables robotic systems to navigate along diverse trajectories while maintaining consistent performance values, addressing a fundamental challenge in autonomous motion planning. Although his citation count is currently modest at 2, Moiseenko’s contribution is notable for bridging the gap between evolutionary optimization and modern reinforcement learning techniques—a promising direction for adaptive robotics. His work is particularly relevant for researchers exploring sample-efficient control in dynamic environments, where traditional methods often struggle. As the field of learning-based control continues to expand, Moiseenko’s integration of evolutionary algorithms into reinforcement learning frameworks offers a practical pathway for developing more robust and versatile robotic controllers.