The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning. - Volodymyr Tkachuk; Sriram Ganapathi Subramanian; Matthew E. Taylor (CoRR, 2021)

Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning. - Volodymyr Tkachuk; Seyed Alireza Bakhtiari; Johannes Kirschner; Matej Jusup; Ilija Bogunovic; Csaba Szepesvári (AISTATS, 2023)

Regret Minimization via Saddle Point Optimization. - Johannes Kirschner; Seyed Alireza Bakhtiari; Kushagra Chandak; Volodymyr Tkachuk; Csaba Szepesvári (NeurIPS, 2023)

Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning. - Matthew Schlegel; Volodymyr Tkachuk; Adam M. White; Martha White (Trans. Mach. Learn. Res., 2023)

Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear qπ-Realizability and Concentrability. - Volodymyr Tkachuk; Gellért Weisz; Csaba Szepesvári (NeurIPS, 2024)