10 citations · 31 across the 14 of their papers we have counts for
14 papers
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…
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…
ChiPFormer: Transferable Chip Placement via Offline Decision Transformer
Yao Lai, Jinxin Liu, Zhentao Tang +3
Placement is a critical step in modern chip design, aiming to determine the positions of circuit modules on the chip canvas. Recent works have shown that reinforcement learning (RL…
STRAPPER: Preference-based Reinforcement Learning via Self-training Augmentation and Peer Regularization
Yachen Kang, Li He, Jinxin Liu +2
Preference-based reinforcement learning (PbRL) promises to learn a complex reward function with binary human preference. However, such human-in-the-loop formulation requires consid…
Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement Learning
Jinxin Liu, Ziqi Zhang, Zhenyu Wei +4
Offline reinforcement learning (RL) aims to learn a policy using only pre-collected and fixed data. Although avoiding the time-consuming online interactions in RL, it poses challen…
Beyond Reward: Offline Preference-guided Policy Optimization
Yachen Kang, Diyuan Shi, Jinxin Liu +2
This study focuses on the topic of offline preference-based reinforcement learning (PbRL), a variant of conventional reinforcement learning that dispenses with the need for online…
CEIL: Generalized Contextual Imitation Learning
Jinxin Liu, Li He, Yachen Kang +3
In this paper, we present \textbf{C}ont\textbf{E}xtual \textbf{I}mitation \textbf{L}earning~(CEIL), a general and broadly applicable algorithm for imitation learning (IL). Inspired…
Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization
Jinxin Liu, Hongyin Zhang, Zifeng Zhuang +3
In this work, we decouple the iterative bi-level offline RL (value estimation and policy extraction) from the offline training phase, forming a non-iterative bi-level paradigm and…
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning
Jinxin Liu, Lipeng Zu, Li He +1
Offline reinforcement learning (RL) aims to learn an optimal policy from pre-collected and labeled datasets, which eliminates the time-consuming data collection in online RL. Howev…
Behavior Proximal Policy Optimization
Zifeng Zhuang, Kun Lei, Jinxin Liu +2
Offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-critic methods perform poorly due to the overestimation of out-of-distribution state-ac…
DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning
Jinxin Liu, Hongyin Zhang, Donglin Wang
Offline reinforcement learning algorithms promise to be applicable in settings where a fixed dataset is available and no new experience can be acquired. However, such formulation i…