Saved in:
| Main Authors: | Hu, Tianmeng, Cui, Yongzheng, Luo, Biao, Li, Ke |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.16548 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Beyond Monotonicity: Revisiting Factorization Principles in Multi-Agent Q-Learning
by: Hu, Tianmeng, et al.
Published: (2025)
by: Hu, Tianmeng, et al.
Published: (2025)
PA2D-MORL: Pareto Ascent Directional Decomposition based Multi-Objective Reinforcement Learning
by: Hu, Tianmeng, et al.
Published: (2026)
by: Hu, Tianmeng, et al.
Published: (2026)
MO-MIX: Multi-Objective Multi-Agent Cooperative Decision-Making With Deep Reinforcement Learning
by: Hu, Tianmeng, et al.
Published: (2026)
by: Hu, Tianmeng, et al.
Published: (2026)
Uncertainty-Aware Multi-Objective Reinforcement Learning-Guided Diffusion Models for 3D De Novo Molecular Design
by: Chen, Lianghong, et al.
Published: (2025)
by: Chen, Lianghong, et al.
Published: (2025)
Inverse Design of Metamaterials with Manufacturing-Guiding Spectrum-to-Structure Conditional Diffusion Model
by: Li, Jiawen, et al.
Published: (2025)
by: Li, Jiawen, et al.
Published: (2025)
Environment Design for Inverse Reinforcement Learning
by: Buening, Thomas Kleine, et al.
Published: (2022)
by: Buening, Thomas Kleine, et al.
Published: (2022)
GenPO: Generative Diffusion Models Meet On-Policy Reinforcement Learning
by: Ding, Shutong, et al.
Published: (2025)
by: Ding, Shutong, et al.
Published: (2025)
Learning To Sample From Diffusion Models Via Inverse Reinforcement Learning
by: Bourdrez, Constant, et al.
Published: (2026)
by: Bourdrez, Constant, et al.
Published: (2026)
CausalGDP: Causality-Guided Diffusion Policies for Reinforcement Learning
by: Xiao, Xiaofeng, et al.
Published: (2026)
by: Xiao, Xiaofeng, et al.
Published: (2026)
Multi-Mode Process Control Using Multi-Task Inverse Reinforcement Learning
by: Lin, Runze, et al.
Published: (2025)
by: Lin, Runze, et al.
Published: (2025)
Distributional Reinforcement Learning with Diffusion Bridge Critics
by: Ding, Shutong, et al.
Published: (2026)
by: Ding, Shutong, et al.
Published: (2026)
Inverse Reinforcement Learning without Reinforcement Learning
by: Swamy, Gokul, et al.
Published: (2023)
by: Swamy, Gokul, et al.
Published: (2023)
Guided Diffusion for Fast Inverse Design of Density-based Mechanical Metamaterials
by: Yang, Yanyan, et al.
Published: (2024)
by: Yang, Yanyan, et al.
Published: (2024)
PAGAR: Taming Reward Misalignment in Inverse Reinforcement Learning-Based Imitation Learning with Protagonist Antagonist Guided Adversarial Reward
by: Zhou, Weichao, et al.
Published: (2023)
by: Zhou, Weichao, et al.
Published: (2023)
Distributional Inverse Reinforcement Learning
by: Wu, Feiyang, et al.
Published: (2025)
by: Wu, Feiyang, et al.
Published: (2025)
Prior-Guided Diffusion Planning for Offline Reinforcement Learning
by: Ki, Donghyeon, et al.
Published: (2025)
by: Ki, Donghyeon, et al.
Published: (2025)
Sample-Efficient Diffusion-based Reinforcement Learning with Critic Guidance
by: Ding, Shutong, et al.
Published: (2026)
by: Ding, Shutong, et al.
Published: (2026)
Inverse Design in Distributed Circuits Using Single-Step Reinforcement Learning
by: Li, Jiayu, et al.
Published: (2025)
by: Li, Jiayu, et al.
Published: (2025)
How Does the Lagrangian Guide Safe Reinforcement Learning through Diffusion Models?
by: Cheng, Xiaoyuan, et al.
Published: (2026)
by: Cheng, Xiaoyuan, et al.
Published: (2026)
Inverse Reinforcement Learning with Switching Rewards and History Dependency for Characterizing Animal Behaviors
by: Ke, Jingyang, et al.
Published: (2025)
by: Ke, Jingyang, et al.
Published: (2025)
Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits
by: Mahlau, Yannik, et al.
Published: (2025)
by: Mahlau, Yannik, et al.
Published: (2025)
Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model
by: Zheng, Yinan, et al.
Published: (2024)
by: Zheng, Yinan, et al.
Published: (2024)
Towards Generalized Inverse Reinforcement Learning
by: Dong, Chaosheng, et al.
Published: (2024)
by: Dong, Chaosheng, et al.
Published: (2024)
The Virtues of Pessimism in Inverse Reinforcement Learning
by: Wu, David, et al.
Published: (2024)
by: Wu, David, et al.
Published: (2024)
Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning
by: Liu, Xu-Hui, et al.
Published: (2024)
by: Liu, Xu-Hui, et al.
Published: (2024)
Diffusion-based Reinforcement Learning via Q-weighted Variational Policy Optimization
by: Ding, Shutong, et al.
Published: (2024)
by: Ding, Shutong, et al.
Published: (2024)
Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models
by: Yoon, Sangwoong, et al.
Published: (2024)
by: Yoon, Sangwoong, et al.
Published: (2024)
Hybrid Inverse Reinforcement Learning
by: Ren, Juntao, et al.
Published: (2024)
by: Ren, Juntao, et al.
Published: (2024)
A Bayesian Approach to Robust Inverse Reinforcement Learning
by: Wei, Ran, et al.
Published: (2023)
by: Wei, Ran, et al.
Published: (2023)
Advantage-Guided Diffusion for Model-Based Reinforcement Learning
by: Foffano, Daniele, et al.
Published: (2026)
by: Foffano, Daniele, et al.
Published: (2026)
Diffusion Guided Adversarial State Perturbations in Reinforcement Learning
by: Sun, Xiaolin, et al.
Published: (2025)
by: Sun, Xiaolin, et al.
Published: (2025)
RTLSeek: Boosting the LLM-Based RTL Generation with Multi-Stage Diversity-Oriented Reinforcement Learning
by: Zhang, Xinyu, et al.
Published: (2026)
by: Zhang, Xinyu, et al.
Published: (2026)
Scalable Multiagent Reinforcement Learning with Collective Influence Estimation
by: Luo, Zhenglong, et al.
Published: (2026)
by: Luo, Zhenglong, et al.
Published: (2026)
Kernel Density Bayesian Inverse Reinforcement Learning
by: Mandyam, Aishwarya, et al.
Published: (2023)
by: Mandyam, Aishwarya, et al.
Published: (2023)
Labeled TrustSet Guided: Batch Active Learning with Reinforcement Learning
by: Cui, Guofeng, et al.
Published: (2026)
by: Cui, Guofeng, et al.
Published: (2026)
Quantifying the Sensitivity of Inverse Reinforcement Learning to Misspecification
by: Skalse, Joar, et al.
Published: (2024)
by: Skalse, Joar, et al.
Published: (2024)
Inverse Reinforcement Learning with Multiple Planning Horizons
by: Yao, Jiayu, et al.
Published: (2024)
by: Yao, Jiayu, et al.
Published: (2024)
Accelerating Inverse Reinforcement Learning with Expert Bootstrapping
by: Wu, David, et al.
Published: (2024)
by: Wu, David, et al.
Published: (2024)
Walking the Values in Bayesian Inverse Reinforcement Learning
by: Bajgar, Ondrej, et al.
Published: (2024)
by: Bajgar, Ondrej, et al.
Published: (2024)
Confidence Aware Inverse Constrained Reinforcement Learning
by: Subramanian, Sriram Ganapathi, et al.
Published: (2024)
by: Subramanian, Sriram Ganapathi, et al.
Published: (2024)
Similar Items
-
Beyond Monotonicity: Revisiting Factorization Principles in Multi-Agent Q-Learning
by: Hu, Tianmeng, et al.
Published: (2025) -
PA2D-MORL: Pareto Ascent Directional Decomposition based Multi-Objective Reinforcement Learning
by: Hu, Tianmeng, et al.
Published: (2026) -
MO-MIX: Multi-Objective Multi-Agent Cooperative Decision-Making With Deep Reinforcement Learning
by: Hu, Tianmeng, et al.
Published: (2026) -
Uncertainty-Aware Multi-Objective Reinforcement Learning-Guided Diffusion Models for 3D De Novo Molecular Design
by: Chen, Lianghong, et al.
Published: (2025) -
Inverse Design of Metamaterials with Manufacturing-Guiding Spectrum-to-Structure Conditional Diffusion Model
by: Li, Jiawen, et al.
Published: (2025)