Miao Liu

Learning Hierarchical Teaching Policies for Cooperative Agents

2019 · arXiv (Cornell University)

Collective learning can be greatly enhanced when agents effectively exchange knowledge with their peers. In particular, recent work studying agents that learn to teach other teammates has demonstrated that action advising accelerates team-wide learning. However, …

Context-Specific Representation Abstraction for Deep Option Learning

2021 · arXiv (Cornell University)

Hierarchical reinforcement learning has focused on discovering temporally extended actions, such as options, that can provide benefits in problems requiring extensive exploration. One promising approach that learns these options end-to-end is the option-critic (OC) framework. …

Local Explanations for Reinforcement Learning

2023 · Proceedings of the AAAI Conference on Artificial Intelligence

Many works in explainable AI have focused on explaining black-box classification models. Explaining deep reinforcement learning (RL) policies in a manner that could be understood by domain users has received much less attention. In this …

Q-function Decomposition with Intervention Semantics with Factored Action Spaces

2025 · arXiv (Cornell University)

Many practical reinforcement learning environments have a discrete factored action space that induces a large combinatorial set of actions, thereby posing significant challenges. Existing approaches leverage the regular structure of the action space and resort …