Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximation
Fuente:
arXiv
Saved in:
| Main Authors: | Xu, Tian, Zhang, Zhilong, Chen, Ruishuo, Sun, Yihao, Yu, Yang |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Adversarial Imitation Learning with General Function Approximation: Theoretical Analysis and Practical Algorithms
by: Xu, Tian, et al.
Published: (2026)
by: Xu, Tian, et al.
Published: (2026)
Non-Adversarial Imitation Learning Provably Free of Compounding Errors: The Role of Bellman Constraints
by: Xu, Tian, et al.
Published: (2026)
by: Xu, Tian, et al.
Published: (2026)
Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees
by: Chen, Yilei, et al.
Published: (2024)
by: Chen, Yilei, et al.
Published: (2024)
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
by: Chen, Yu, et al.
Published: (2024)
by: Chen, Yu, et al.
Published: (2024)
Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation
by: Li, Long-Fei, et al.
Published: (2024)
by: Li, Long-Fei, et al.
Published: (2024)
Provably Efficient Offline-to-Online Value Adaptation with General Function Approximation
by: Li, Shangzhe, et al.
Published: (2026)
by: Li, Shangzhe, et al.
Published: (2026)
Understanding Adversarial Imitation Learning in Small Sample Regime: A Stage-coupled Analysis
by: Xu, Tian, et al.
Published: (2022)
by: Xu, Tian, et al.
Published: (2022)
Provable Unrestricted Adversarial Training without Compromise with Generalizability
by: Zhang, Lilin, et al.
Published: (2023)
by: Zhang, Lilin, et al.
Published: (2023)
Auto-Encoding Adversarial Imitation Learning
by: Zhang, Kaifeng, et al.
Published: (2022)
by: Zhang, Kaifeng, et al.
Published: (2022)
Latent Wasserstein Adversarial Imitation Learning
by: Yang, Siqi, et al.
Published: (2026)
by: Yang, Siqi, et al.
Published: (2026)
Learning Equilibria from Data: Provably Efficient Multi-Agent Imitation Learning
by: Freihaut, Till, et al.
Published: (2025)
by: Freihaut, Till, et al.
Published: (2025)
Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation
by: Cho, Taehyun, et al.
Published: (2024)
by: Cho, Taehyun, et al.
Published: (2024)
Interactive and Hybrid Imitation Learning: Provably Beating Behavior Cloning
by: Li, Yichen, et al.
Published: (2024)
by: Li, Yichen, et al.
Published: (2024)
Optimistically Optimistic Exploration for Provably Efficient Infinite-Horizon Reinforcement and Imitation Learning
by: Moulin, Antoine, et al.
Published: (2025)
by: Moulin, Antoine, et al.
Published: (2025)
Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation
by: Liu, Zhishuai, et al.
Published: (2024)
by: Liu, Zhishuai, et al.
Published: (2024)
Diffusion-Reward Adversarial Imitation Learning
by: Lai, Chun-Mao, et al.
Published: (2024)
by: Lai, Chun-Mao, et al.
Published: (2024)
Towards Practical World Model-based Reinforcement Learning for Vision-Language-Action Models
by: Zhang, Zhilong, et al.
Published: (2026)
by: Zhang, Zhilong, et al.
Published: (2026)
Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation
by: Sheebaelhamd, Ziyad, et al.
Published: (2026)
by: Sheebaelhamd, Ziyad, et al.
Published: (2026)
Any-step Dynamics Model Improves Future Predictions for Online and Offline Reinforcement Learning
by: Lin, Haoxin, et al.
Published: (2024)
by: Lin, Haoxin, et al.
Published: (2024)
Adversarial Imitation Learning via Boosting
by: Chang, Jonathan D., et al.
Published: (2024)
by: Chang, Jonathan D., et al.
Published: (2024)
Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation
by: Zhao, Runze, et al.
Published: (2025)
by: Zhao, Runze, et al.
Published: (2025)
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
by: Ishfaq, Haque, et al.
Published: (2023)
by: Ishfaq, Haque, et al.
Published: (2023)
C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory
by: Luo, Tianjiao, et al.
Published: (2024)
by: Luo, Tianjiao, et al.
Published: (2024)
Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency
by: Cai, Qi, et al.
Published: (2022)
by: Cai, Qi, et al.
Published: (2022)
Provably Efficient Partially Observable Risk-Sensitive Reinforcement Learning with Hindsight Observation
by: Zhang, Tonghe, et al.
Published: (2024)
by: Zhang, Tonghe, et al.
Published: (2024)
PowerFlow: Unlocking the Dual Nature of LLMs via Principled Distribution Matching
by: Chen, Ruishuo, et al.
Published: (2026)
by: Chen, Ruishuo, et al.
Published: (2026)
Provably Efficient RL under Episode-Wise Safety in Constrained MDPs with Linear Function Approximation
by: Kitamura, Toshinori, et al.
Published: (2025)
by: Kitamura, Toshinori, et al.
Published: (2025)
Provably Efficient Infinite-Horizon Average-Reward Reinforcement Learning with Linear Function Approximation
by: Chae, Woojin, et al.
Published: (2024)
by: Chae, Woojin, et al.
Published: (2024)
Physics-informed Imitative Reinforcement Learning for Real-world Driving
by: Zhou, Hang, et al.
Published: (2024)
by: Zhou, Hang, et al.
Published: (2024)
On the Benefits of Inducing Local Lipschitzness for Robust Generative Adversarial Imitation Learning
by: Memarian, Farzan, et al.
Published: (2021)
by: Memarian, Farzan, et al.
Published: (2021)
Offline Imitation Learning with Variational Counterfactual Reasoning
by: He, Bowei, et al.
Published: (2023)
by: He, Bowei, et al.
Published: (2023)
Provably Efficient and Agile Randomized Q-Learning
by: Wang, He, et al.
Published: (2025)
by: Wang, He, et al.
Published: (2025)
Multi-Agent Generative Adversarial Interactive Self-Imitation Learning for AUV Formation Control and Obstacle Avoidance
by: Fang, Zheng, et al.
Published: (2024)
by: Fang, Zheng, et al.
Published: (2024)
Sample-efficient Adversarial Imitation Learning
by: Jung, Dahuin, et al.
Published: (2023)
by: Jung, Dahuin, et al.
Published: (2023)
Your Self-Play Algorithm is Secretly an Adversarial Imitator: Understanding LLM Self-Play through the Lens of Imitation Learning
by: Li, Shangzhe, et al.
Published: (2026)
by: Li, Shangzhe, et al.
Published: (2026)
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)
Corruption-Robust Offline Reinforcement Learning with General Function Approximation
by: Ye, Chenlu, et al.
Published: (2023)
by: Ye, Chenlu, et al.
Published: (2023)
Agnostic Interactive Imitation Learning: New Theory and Practical Algorithms
by: Li, Yichen, et al.
Published: (2023)
by: Li, Yichen, et al.
Published: (2023)
PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization
by: Ye, Yuyang, et al.
Published: (2024)
by: Ye, Yuyang, et al.
Published: (2024)
Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in RL
by: Liu, Xiangyu, et al.
Published: (2023)
by: Liu, Xiangyu, et al.
Published: (2023)
Similar Items
-
Adversarial Imitation Learning with General Function Approximation: Theoretical Analysis and Practical Algorithms
by: Xu, Tian, et al.
Published: (2026) -
Non-Adversarial Imitation Learning Provably Free of Compounding Errors: The Role of Bellman Constraints
by: Xu, Tian, et al.
Published: (2026) -
Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees
by: Chen, Yilei, et al.
Published: (2024) -
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
by: Chen, Yu, et al.
Published: (2024) -
Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation
by: Li, Long-Fei, et al.
Published: (2024)