1 citations · 1 across the 9 of their papers we have counts for
9 papers
Boundary-to-Region Supervision for Offline Safe Reinforcement Learning
Huikang Su, Dengyun Peng, Zifeng Zhuang +4
Offline safe reinforcement learning aims to learn policies that satisfy predefined safety constraints from static datasets. Existing sequence-model-based methods condition action g…
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…
DIDI: Diffusion-Guided Diversity for Offline Behavioral Generation
Jinxin Liu, Xinghong Guo, Zifeng Zhuang +1
In this paper, we propose a novel approach called DIffusion-guided DIversity (DIDI) for offline behavioral generation. The goal of DIDI is to learn a diverse set of skills from a m…
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…
Unlock Reliable Skill Inference for Quadruped Adaptive Behavior by Skill Graph
Hongyin Zhang, Diyuan Shi, Zifeng Zhuang +6
Developing robotic intelligent systems that can adapt quickly to unseen wild situations is one of the critical challenges in pursuing autonomous robotics. Although some impressive…
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…