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

The performance of modern reinforcement learning algorithms critically relies on tuning ever increasing numbers of hyperparameters.

CAS stands for Chinese Academy of Sciences 38th Conference on Neural Information Processing Systems (NeurIPS 2024).

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.

OFUGLB outperforms or is at par with prior algorithms for logistic bandits.

Intelligent agents must be generalists, capable of quickly adapting to various tasks.

This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks.

Significant progress has been made inthepast decade thanks to the availability of pedestrian trajectory datasets, which enable trajectory prediction methods to learn from pedestrians' past movements and predict future trajectories. However, these datasets and methods typically assume that theobservedtrajectory sequence iscomplete, ignoring real-world issues such as sensor failure, occlusion, and limited fields of view that can result in missing valuesinobservedtrajectories.