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Found 4 papers, 0 papers with code
Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear $q^π$-Realizability and Concentrability
The hope in this setting is that learning a good policy will be possible without requiring a sample size that scales with the number of states in the MDP.
Regret Minimization via Saddle Point Optimization
A long line of works characterizes the sample complexity of regret minimization in sequential decision-making by min-max programs.
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning
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
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.
