27 citations · 86 across the 44 of their papers we have counts for
25 papers · 1 filter
ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow
Dongxiu Liu, Haoyi Niu, Peng Cheng +5
In the physical world we inhabit, space and time are fundamentally continuous. However, existing machine learning paradigms for world modeling are largely confined to discrete-time…
Horizon Adaptive Offline Policy Learning via Value Stitching
Kexin Zheng, Xianyuan Zhan, Xintao Yan
Learning accurate value functions plays a decisive role for reinforcement learning (RL) agents to solve long-horizon, complex tasks. Conventional temporal-difference (TD) learning…
Unraveling the Hidden Dynamical Structure in Recurrent Neural Policies
Jin Li, Yue Wu, Mengsha Huang +3
Recurrent neural policies are widely used in partially observable control and meta-RL tasks. Their abilities to maintain internal memory and adapt quickly to unseen scenarios have…
Dichotomous Diffusion Policy Optimization
Ruiming Liang, Yinan Zheng, Kexin Zheng +9
Diffusion-based policies have gained growing popularity in solving a wide range of decision-making tasks due to their superior expressiveness and controllable generation during inf…
Skill Expansion and Composition in Parameter Space
Tenglong Liu, Jianxiong Li, Yinan Zheng +4
Humans excel at reusing prior knowledge to address new challenges and developing skills while solving problems. This paradigm becomes increasingly popular in the development of aut…
Are Expressive Models Truly Necessary for Offline RL?
Guan Wang, Haoyi Niu, Jianxiong Li +3
Among various branches of offline reinforcement learning (RL) methods, goal-conditioned supervised learning (GCSL) has gained increasing popularity as it formulates the offline RL…
Diffusion-DICE: In-Sample Diffusion Guidance for Offline Reinforcement Learning
Liyuan Mao, Haoran Xu, Xianyuan Zhan +2
One important property of DIstribution Correction Estimation (DICE) methods is that the solution is the optimal stationary distribution ratio between the optimized and data collect…
Policy Bifurcation in Safe Reinforcement Learning
Wenjun Zou, Yao Lyu, Jie Li +7
Safe reinforcement learning (RL) offers advanced solutions to constrained optimal control problems. Existing studies in safe RL implicitly assume continuity in policy functions, wh…
ODICE: Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient Update
Liyuan Mao, Haoran Xu, Weinan Zhang +1
In this study, we investigate the DIstribution Correction Estimation (DICE) methods, an important line of work in offline reinforcement learning (RL) and imitation learning (IL). D…
Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model
Yinan Zheng, Jianxiong Li, Dongjie Yu +4
Safe offline RL is a promising way to bypass risky online interactions towards safe policy learning. Most existing methods only enforce soft constraints, i.e., constraining safety…
FlexSSL : A Generic and Efficient Framework for Semi-Supervised Learning
Huiling Qin, Xianyuan Zhan, Yuanxun Li +1
Semi-supervised learning holds great promise for many real-world applications, due to its ability to leverage both unlabeled and expensive labeled data. However, most semi-supervis…
H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps
Haoyi Niu, Tianying Ji, Bingqi Liu +7
Solving real-world complex tasks using reinforcement learning (RL) without high-fidelity simulation environments or large amounts of offline data can be quite challenging. Online R…
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization
Xiangsen Wang, Haoran Xu, Yinan Zheng +1
Offline reinforcement learning (RL) has received considerable attention in recent years due to its attractive capability of learning policies from offline datasets without environm…
Offline Multi-Agent Reinforcement Learning with Coupled Value Factorization
Xiangsen Wang, Xianyuan Zhan
Offline reinforcement learning (RL) that learns policies from offline datasets without environment interaction has received considerable attention in recent years. Compared with th…
Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL
Peng Cheng, Xianyuan Zhan, Zhihao Wu +5
Offline reinforcement learning (RL) offers an appealing approach to real-world tasks by learning policies from pre-collected datasets without interacting with the environment. Howe…
Feasible Policy Iteration for Safe Reinforcement Learning
Yujie Yang, Zhilong Zheng, Shengbo Eben Li +4
Safety is the priority concern when applying reinforcement learning (RL) algorithms to real-world control problems. While policy iteration provides a fundamental algorithm for stan…
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization
Haoran Xu, Li Jiang, Jianxiong Li +4
Most offline reinforcement learning (RL) methods suffer from the trade-off between improving the policy to surpass the behavior policy and constraining the policy to limit the devi…
Mind the Gap: Offline Policy Optimization for Imperfect Rewards
Jianxiong Li, Xiao Hu, Haoran Xu +4
Reward function is essential in reinforcement learning (RL), serving as the guiding signal to incentivize agents to solve given tasks, however, is also notoriously difficult to des…
A Policy-Guided Imitation Approach for Offline Reinforcement Learning
Haoran Xu, Li Jiang, Jianxiong Li +1
Offline reinforcement learning (RL) methods can generally be categorized into two types: RL-based and Imitation-based. RL-based methods could in principle enjoy out-of-distribution…
Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations
Haoran Xu, Xianyuan Zhan, Honglei Yin +1
We study the problem of offline Imitation Learning (IL) where an agent aims to learn an optimal expert behavior policy without additional online environment interactions. Instead,…
Discriminator-Guided Model-Based Offline Imitation Learning
Wenjia Zhang, Haoran Xu, Haoyi Niu +5
Offline imitation learning (IL) is a powerful method to solve decision-making problems from expert demonstrations without reward labels. Existing offline IL methods suffer from sev…
When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning
Haoyi Niu, Shubham Sharma, Yiwen Qiu +4
Learning effective reinforcement learning (RL) policies to solve real-world complex tasks can be quite challenging without a high-fidelity simulation environment. In most cases, we…
When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning
Jianxiong Li, Xianyuan Zhan, Haoran Xu +3
In offline reinforcement learning (RL), one detrimental issue to policy learning is the error accumulation of deep Q function in out-of-distribution (OOD) areas. Unfortunately, exi…
Constraints Penalized Q-learning for Safe Offline Reinforcement Learning
Haoran Xu, Xianyuan Zhan, Xiangyu Zhu
We study the problem of safe offline reinforcement learning (RL), the goal is to learn a policy that maximizes long-term reward while satisfying safety constraints given only offli…