Lantao Yu

Multi-Agent Adversarial Inverse Reinforcement Learning.

2019 · arXiv (Cornell University)

Reinforcement learning agents are prone to undesired behaviors due to reward mis-specification. Finding a set of reward functions to properly guide agent behaviors is particularly challenging in multi-agent scenarios. Inverse reinforcement learning provides a framework …

A Unified Framework for Multi-distribution Density Ratio Estimation

2021 · arXiv (Cornell University)

Binary density ratio estimation (DRE), the problem of estimating the ratio $p_1/p_2$ given their empirical samples, provides the foundation for many state-of-the-art machine learning algorithms such as contrastive representation learning and covariate shift adaptation. In …