6 citations · 15 across the 12 of their papers we have counts for
5 papers · 1 filter
NeSyFS: A Neuro-symbolic Fast-Slow Thinking Framework for LLM Agent under Partial Observability
Duo Xu, Faramarz Fekri
Recently Large Language Models (LLMs) have been increasingly deployed as autonomous agents in applications such as self-reflection, retrieval-augmented generation, and scientific d…
Reinforcement Learning-Augmented LLM Agents for Collaborative Decision Making and Performance Optimization
Dong Qiu, Duo Xu, Limengxi Yue
Large Language Models (LLMs) perform well in language tasks but often lack collaborative awareness and struggle to optimize global performance in multi-agent settings. We present a…
Generalization of Compositional Tasks with Logical Specification via Implicit Planning
Duo Xu, Faramarz Fekri
In this study, we address the challenge of learning generalizable policies for compositional tasks defined by logical specifications. These tasks consist of multiple temporally ext…
LLM-Augmented Symbolic Reinforcement Learning with Landmark-Based Task Decomposition
Alireza Kheirandish, Duo Xu, Faramarz Fekri
One of the fundamental challenges in reinforcement learning (RL) is to take a complex task and be able to decompose it to subtasks that are simpler for the RL agent to learn. In th…
Generalizing LTL Instructions via Future Dependent Options
Duo Xu, Faramarz Fekri
In many real-world applications of control system and robotics, linear temporal logic (LTL) is a widely-used task specification language which has a compositional grammar that natu…