Andrey Kolobov

Papers

Total Citations

H-Index

About

Andrey Kolobov is a leading researcher in artificial intelligence, with a primary focus on sequential decision-making under uncertainty, particularly in the domains of planning, reinforcement learning, and robotics. His most influential work, "Classical Planning in MDP Heuristics: with a Little Help from Generalization" (2010), has garnered 23 citations and addresses the critical challenge of computing effective policies in stochastic environments with unknown dynamics and reward models. Kolobov’s major contributions lie in developing algorithms that bridge classical planning and model-based reinforcement learning, enabling agents to generalize from limited data and perform robustly in complex, real-world settings such as space robotics and epilepsy management. His research has significantly advanced the practical deployment of AI systems that must operate reliably despite incomplete information. Kolobov’s work is widely recognized for its theoretical depth and practical impact, making him a key figure in the ongoing effort to create intelligent systems capable of autonomous decision-making in uncertain, high-stakes environments.