We present the first finite-sample analysis for policy evaluation in robust average-reward reinforcement learning using semi-norm contractions.

A robust RL framework for quantitative trading with introducing novel elliptic uncertainty sets to model the market impact.

We introduce Forecasting in Non-stationary Offline RL (FORL), a novel framework designed to be robust to passive non-stationarities, leveraging diffusion probabilistic models and time-series forecasting foundation models.