2 citations · 3 across the 11 of their papers we have counts for
11 papers
HyMem: Hierarchical Context Management for Long-Horizon Agents via Information Isolation
XinQi Wang, Jinwei Xiao, Sijia Cui +4
Large language model (LLM) agents often perform poorly on complex, long-horizon tasks because their context becomes increasingly cluttered over time. As interactions accumulate, de…
Coarse-to-Fine Grounded Memory for LLM Agent Planning
Wei Yang, Jinwei Xiao, Hongming Zhang +3
Recent advancements in Large Language Models (LLMs) have driven growing interest in LLM-based agents for complex planning tasks. To avoid costly agent training, many studies adopte…
Self-Guided Function Calling in Large Language Models via Stepwise Experience Recall
Sijia Cui, Aiyao He, Shuai Xu +5
Function calling enables large language models (LLMs) to interact with external systems by leveraging tools and APIs. When faced with multi-step tool usage, LLMs still struggle wit…
-DQN: Improving Deep Q-Learning By Evolving the Behavior
Hongming Zhang, Fengshuo Bai, Chenjun Xiao +3
While many sophisticated exploration methods have been proposed, their lack of generality and high computational cost often lead researchers to favor simpler methods like -greed…
Efficient Preference-based Reinforcement Learning via Aligned Experience Estimation
Fengshuo Bai, Rui Zhao, Hongming Zhang +5
Preference-based reinforcement learning (PbRL) has shown impressive capabilities in training agents without reward engineering. However, a notable limitation of PbRL is its depende…
Monte Carlo Tree Search in the Presence of Transition Uncertainty
Farnaz Kohankhaki, Kiarash Aghakasiri, Hongming Zhang +3
Monte Carlo Tree Search (MCTS) is an immensely popular search-based framework used for decision making. It is traditionally applied to domains where a perfect simulation model of t…
Provable Representation with Efficient Planning for Partial Observable Reinforcement Learning
Hongming Zhang, Tongzheng Ren, Chenjun Xiao +2
In most real-world reinforcement learning applications, state information is only partially observable, which breaks the Markov decision process assumption and leads to inferior pe…
Build generally reusable agent-environment interaction models
Jun Jin, Hongming Zhang, Jun Luo
This paper tackles the problem of how to pre-train a model and make it generally reusable backbones for downstream task learning. In pre-training, we propose a method that builds a…
A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning
Hongming Zhang, Ke Sun, Bo Xu +2
In deep reinforcement learning (RL) systems, abnormal states pose significant risks by potentially triggering unpredictable behaviors and unsafe actions, thus impeding the deployme…
Efficient Reinforcement Learning Development with RLzoo
Zihan Ding, Tianyang Yu, Yanhua Huang +5
Many researchers and developers are exploring for adopting Deep Reinforcement Learning (DRL) techniques in their applications. They however often find such an adoption challenging.…
Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning
Jiancheng Long, Hongming Zhang, Tianyang Yu +1
Multi-agent systems have a wide range of applications in cooperative and competitive tasks. As the number of agents increases, nonstationarity gets more serious in multi-agent rein…