1 citations · 1 across the 6 of their papers we have counts for
6 papers
Nash CoT: Multi-Path Inference with Preference Equilibrium
Ziqi Zhang, Cunxiang Wang, Xiong Xiao +2
Chain of thought (CoT) is a reasoning framework that can enhance the performance of Large Language Models (LLMs) on complex inference tasks. In particular, among various studies re…
Reinformer: Max-Return Sequence Modeling for Offline RL
Zifeng Zhuang, Dengyun Peng, Jinxin Liu +2
As a data-driven paradigm, offline reinforcement learning (RL) has been formulated as sequence modeling that conditions on the hindsight information including returns, goal or futu…
Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted Regression
Ziqi Zhang, Zifeng Zhuang, Jingzehua Xu +4
We propose a novel one-step supervised imitation learning (IL) framework called Adversarial Density Regression (ADR). This IL framework aims to correct the policy learned on unknow…
Context-Former: Stitching via Latent Conditioned Sequence Modeling
Ziqi Zhang, Jingzehua Xu, Jinxin Liu +4
Offline reinforcement learning (RL) algorithms can learn better decision-making compared to behavior policies by stitching the suboptimal trajectories to derive more optimal ones.…
A dynamical clipping approach with task feedback for Proximal Policy Optimization
Ziqi Zhang, Jingzehua Xu, Zifeng Zhuang +4
Proximal Policy Optimization (PPO) has been broadly applied to robotics learning, showcasing stable training performance. However, the fixed clipping bound setting may limit the pe…
Improving Offline-to-Online Reinforcement Learning with Q Conditioned State Entropy Exploration
Ziqi Zhang, Xiao Xiong, Zifeng Zhuang +2
Studying how to fine-tune offline reinforcement learning (RL) pre-trained policy is profoundly significant for enhancing the sample efficiency of RL algorithms. However, directly f…