Sample efficiencyof ESRL is independent of the chosen risk aversion threshold and quality of the behavior policy.

Offline reinforcement learning (RL) tasks require the agent to learn from a precollected dataset with no further interactions with the environment. Despite the potential tosurpass thebehavioral policies, RL-based methods aregenerally impractical duetothetraining instability andbootstrapping theextrapolation errors, which always require careful hyperparameter tuning via online evaluation.

Offline meta-reinforcement learning (OMRL) utilizes pre-collected offline datasets to enhance the agent's generalization ability on unseen tasks.

We propose a novel preference modeling approach that utilizes AI feedback and only requires a small amount of human data.

Based on this, we propose a novel approach, Diffusion-DICE, that directly performs this transformation using diffusion models.

A/B testing is critical for modern technological companies to evaluate the effectiveness of newly developed products against standard baselines. This paper studies optimal designs that aim to maximize the amount of information obtained from online experiments to estimate treatment effects accurately.