Adversarial Imitation

Adversarial imitation learning (AIL) aims to train agents to mimic expert behavior without explicit reward functions, addressing the challenge of specifying rewards in complex real-world tasks. Current research focuses on improving sample efficiency and robustness by developing novel algorithms, such as those employing boosting, diffusion models, or contrastive learning, and by incorporating techniques from offline reinforcement learning and causal inference to mitigate issues like distributional shift and spurious correlations. These advancements are significant because they enable more efficient and reliable learning from limited expert demonstrations, paving the way for wider application in robotics, control systems, and other fields requiring complex skill acquisition.

Papers

November 15, 2024

Measuring Non-Adversarial Reproduction of Training Data in Large Language Models
Michael Aerni, Javier Rando, Edoardo Debenedetti, Nicholas Carlini, Daphne Ippolito, Florian Tramèr
Large Language Model Language Model Training Data Limited Memorization Adversarial Imitation

November 1, 2024

Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximation
Tian Xu, Zhilong Zhang, Ruishuo Chen, Yihao Sun, Yang Yu
Imitation Learning Online Optimization Adversarial Imitation Learning Based Optimization Optimistic Gradient

October 17, 2024

October 7, 2024

Diffusion Imitation from Observation
Bo-Ruei Huang, Chun-Kai Yang, Chun-Mao Lai, Dai-Jie Wu, Shao-Hua Sun
Policy Learning State of the Art Imitation Observation Centric Adversarial Imitation

May 21, 2024

Efficient Imitation Learning with Conservative World Models
Victor Kolev, Rafael Rafailov, Kyle Hatch, Jiajun Wu, Chelsea Finn
Imitation Learning Policy Learning Adversarial Imitation Policy Sampling

April 12, 2024

Adversarial Imitation Learning via Boosting
Jonathan D. Chang, Dhruv Sreenivas, Yingbing Huang, Kianté Brantley, Wen Sun
Actor Critic Adversarial Imitation Learning Policy Algorithm Adversarial Imitation Policy Adversarial Imitation Learning

December 11, 2023

DiffAIL: Diffusion Adversarial Imitation Learning
Bingzheng Wang, Guoqiang Wu, Teng Pang, Yan Zhang, Yilong Yin
Reinforcement Learning State Action Pair Adversarial Imitation Learning Adversarial Imitation Demonstration Example

September 25, 2023

HumanMimic: Learning Natural Locomotion and Transitions for Humanoid Robot via Wasserstein Adversarial Imitation
Annan Tang, Takuma Hiraoka, Naoki Hiraoka, Fan Shi, Kento Kawaharazuka, Kunio Kojima, Kei Okada, Masayuki Inaba
Humanoid Robot Motion Primitive Human Generated Data Adversarial Imitation Locomotion Skill Locomotion Behavior Event Transition Planning

September 20, 2023

Prompt, Plan, Perform: LLM-based Humanoid Control via Quantized Imitation Learning
Jingkai Sun, Qiang Zhang, Yiqun Duan, Xiaoyang Jiang, Chong Cheng, Renjing Xu
Reinforcement Learning Imitation Learning Humanoid Robot Style PROMPT High Level Plan Single Agent Reinforcement Learning Adversarial Imitation Humanoid Control

August 17, 2023

Regularizing Adversarial Imitation Learning Using Causal Invariance
Ivan Ovinnikov, Joachim M. Buhmann
Adversarial Training Imitation Learning Adversarial Imitation Power Regularization Causal Invariance

July 6, 2023

Policy Contrastive Imitation Learning
Jialei Huang, Zhaoheng Yin, Yingdong Hu, Yang Gao
Imitation Learning Adversarial Imitation Contrastive Imitation

June 11, 2023

Provably Efficient Adversarial Imitation Learning with Unknown Transitions
Tian Xu, Ziniu Li, Yang Yu, Zhi-Quan Luo
Imitation Learning Sample Complexity Adversarial Imitation Rare Transition

March 14, 2023

Sample-efficient Adversarial Imitation Learning
Dahuin Jung, Hyungyu Lee, Sungroh Yoon
Imitation Learning Self Supervised Representation Learning Action Representation Adversarial Imitation

February 13, 2023

Unlabeled Imperfect Demonstrations in Adversarial Imitation Learning
Yunke Wang, Bo Du, Chang Xu
Imitation Learning Expert Demonstration Adversarial Imitation Learning Adversarial Imitation

January 31, 2023

PADL: Language-Directed Physics-Based Character Control
Jordan Juravsky, Yunrong Guo, Sanja Fidler, Xue Bin Peng
Physic Based Natural Language Command Adversarial Imitation Simulated Humanoid Typical Control Programming Language

December 30, 2022

Learning from Guided Play: Improving Exploration for Adversarial Imitation Learning with Simple Auxiliary Tasks
Trevor Ablett, Bryan Chan, Jonathan Kelly
LeArning Abstract Imitation Learning Multi Task Environment Exploration Online Reinforcement Learning State Action Pair Adversarial Imitation Learning Adversarial Imitation

September 14, 2022

Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models
Jiawei Liu, Yangyang Kang, Di Tang, Kaisong Song, Changlong Sun, Xiaofeng Wang, Wei Lu, Xiaozhong Liu
Adversarial Attack Adversarial Vulnerability Neural Ranker Adversarial Imitation Adversarial Trigger

June 23, 2022

Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations
Chenhao Li, Marin Vlastelica, Sebastian Blaes, Jonas Frey, Felix Grimminger, Georg Martius
Reward Function Generative Adversarial Reinforcement Learning Approach Expert Demonstration Wasserstein Gan Agile Software Adversarial Imitation

February 12, 2022

Robust Learning from Observation with Model Misspecification
Luca Viano, Yu-Ting Huang, Parameswaran Kamalaruban, Craig Innes, Subramanian Ramamoorthy, Adrian Weller
Imitation Learning Robust Learning Robust Reinforcement Learning Model Misspecification Observation Centric Adversarial Imitation Generative Adversarial Imitation Learning