2 citations · 8 across the 12 of their papers we have counts for
5 papers · 1 filter
The Central Role of the Loss Function in Reinforcement Learning
Kaiwen Wang, Nathan Kallus, Wen Sun
This paper illustrates the central role of loss functions in data-driven decision making, providing a comprehensive survey on their influence in cost-sensitive classification (CSC)…
Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes
Andrew Bennett, Nathan Kallus, Miruna Oprescu +2
We study the evaluation of a policy under best- and worst-case perturbations to a Markov decision process (MDP), using transition observations from the original MDP, whether they a…
A Reductions Approach to Risk-Sensitive Reinforcement Learning with Optimized Certainty Equivalents
Kaiwen Wang, Dawen Liang, Nathan Kallus +1
We study risk-sensitive RL where the goal is learn a history-dependent policy that optimizes some risk measure of cumulative rewards. We consider a family of risks called the optim…
Switching the Loss Reduces the Cost in Batch (Offline) Reinforcement Learning
Alex Ayoub, Kaiwen Wang, Vincent Liu +5
We propose training fitted Q-iteration with log-loss (FQI-log) for batch reinforcement learning (RL). We show that the number of samples needed to learn a near-optimal policy with…
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning
Kaiwen Wang, Owen Oertell, Alekh Agarwal +2
In this paper, we prove that Distributional Reinforcement Learning (DistRL), which learns the return distribution, can obtain second-order bounds in both online and offline RL in g…