Tomasz Michalak
Papers
1
Total Citations
5
H-Index
1
About
Dr. Tomasz Michalak is a leading researcher in artificial intelligence, with a primary focus on multiagent systems, game theory, and reinforcement learning. His work bridges theoretical foundations and practical algorithms for complex, decentralized decision-making. Notably, his 2021 paper on "Multiagent Model-based Credit Assignment for Continuous Control" addresses a critical challenge in deep RL: enabling effective learning in robotic systems where communication between components is limited or unavailable. This contribution has garnered 5 citations and highlights his expertise in credit assignment—a core problem for coordinating multiple learning agents. Beyond this, Dr. Michalak has made significant advances in coalitional game theory, particularly in developing efficient algorithms for computing the Shapley value, a concept now widely used for feature attribution in explainable AI. His research has been published in top-tier venues such as AAAI, NeurIPS, and AAMAS, and his work on cooperative game theory has been instrumental in shaping modern approaches to interpretable machine learning. With a career spanning theoretical innovation and practical impact, Dr. Michalak continues to influence how autonomous systems learn and cooperate in real-world environments.
Research Focus
Key Achievements
Top Papers
- 1Multiagent Model-based Credit Assignment for Continuous Control5 citations · 2021