Regret Minimization via Saddle Point Optimization

no code implementations • NeurIPS 2023 • Johannes Kirschner, Seyed Alireza Bakhtiari, Kushagra Chandak, Volodymyr Tkachuk, Csaba Szepesvári

A long line of works characterizes the sample complexity of regret minimization in sequential decision-making by min-max programs.

Decision Making

Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning

no code implementations • 8 Feb 2023 • Volodymyr Tkachuk, Seyed Alireza Bakhtiari, Johannes Kirschner, Matej Jusup, Ilija Bogunovic, Csaba Szepesvári

A practical challenge in reinforcement learning are combinatorial action spaces that make planning computationally demanding.

Multi-agent Reinforcement Learning reinforcement-learning +1

The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning

no code implementations • 7 Mar 2021 • Volodymyr Tkachuk, Sriram Ganapathi Subramanian, Matthew E. Taylor

We aim to bridge the gap between theoretical and empirical work in $Q$-function reuse by providing some theoretical insights on the effectiveness of $Q$-function reuse when applied to the $Q$-learning with UCB-Hoeffding algorithm.

Q-Learning Transfer Learning