7 papers
Exploratory Memory-Augmented LLM Agent via Hybrid On- and Off-Policy Optimization
Zeyuan Liu, Jeonghye Kim, Xufang Luo +2
Exploration remains the key bottleneck for large language model agents trained with reinforcement learning. While prior methods exploit pretrained knowledge, they fail in environme…
Temporal Difference Learning with Constrained Initial Representations
Jiafei Lyu, Jingwen Yang, Zhongjian Qiao +5
Recently, there have been numerous attempts to enhance the sample efficiency of off-policy reinforcement learning (RL) agents when interacting with the environment, including archi…
Coinvisor: An RL-Enhanced Chatbot Agent for Interactive Cryptocurrency Investment Analysis
Chong Chen, Ze Liu, Lingfeng Bao +4
The cryptocurrency market offers significant investment opportunities but faces challenges including high volatility and fragmented information. Data integration and analysis are e…
ADG: Ambient Diffusion-Guided Dataset Recovery for Corruption-Robust Offline Reinforcement Learning
Zeyuan Liu, Zhihe Yang, Jiawei Xu +5
Real-world datasets collected from sensors or human inputs are prone to noise and errors, posing significant challenges for applying offline reinforcement learning (RL). While exis…
Multi-Agent Coordination via Multi-Level Communication
Ziluo Ding, Zeyuan Liu, Zhirui Fang +3
The partial observability and stochasticity in multi-agent settings can be mitigated by accessing more information about others via communication. However, the coordination problem…
CDSA: Conservative Denoising Score-based Algorithm for Offline Reinforcement Learning
Zeyuan Liu, Kai Yang, Xiu Li
Distribution shift is a major obstacle in offline reinforcement learning, which necessitates minimizing the discrepancy between the learned policy and the behavior policy to avoid…