These processes find wide application in the natural sciences and machine learning, but their inference is known to be far from trivial.

The application of reinforcement learning (RL) involving interactions with individuals has grown significantly in recent years.

We study off-policy evaluation (OPE) in partially observable environments with complex observations, with the goal of developing estimators whose guarantee avoids exponential dependence on the horizon.

Specifically, we integrate two essential sub-tasks, i.e., traffic data imputation and decision-making, by leveraging a Partial Rewards Conditioned Diffusion (PRCD) model to prevent missing rewards

We explore online learning in episodic Markov decision processes on non-stationary environments (changing losses and probability transitions).