7 papers · 1 filter
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…
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…