A curated list of awesome imitation learning resources and publications
A curated list of awesome imitation learning (including inverse reinforcement learning and behavior cloning) resources, inspired by awesome-php.
Please feel free to send me pull request or email ([email protected]) to add links.
How Resilient Are Imitation Learning Methods to Sub-optimal Experts?, Gavenski et al., BRACIS 2023
IQ-Learn: Inverse soft-Q Learning for Imitation, D. Garg et al., NeurIPS 2021
Learning from Imperfect Demonstrations from Agents with Varying Dynamics, Z. Cao et al., ICRA 2021
Robust Imitation Learning from Noisy Demonstrations, V. Tangkaratt et al., AISTATS 2021
Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate, Y. Zhang et al., ICML 2020
Provable Representation Learning for Imitation Learning via Bi-level Optimization, S. Arora et al., ICML 2020
Domain Adaptive Imitation Learning, K. Kim et al., ICML 2020
VILD: Variational Imitation Learning with Diverse-quality Demonstrations, V. Tangkaratt et al., ICML 2020
Imitation Learning from Imperfect Demonstration, Y. Wu et al., ICML 2019
A Divergence Minimization Perspective on Imitation Learning Methods, S. Ghasemipour et al., CoRL 2019
Sample-Efficient Imitation Learning via Generative Adversarial Nets, L. Blonde et al., AISTATS 2019
Sample Efficient Imitation Learning for Continuous Control, F. Sasaki et al., ICLR 2019
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation, R. Wang et al., ICML 2019
Uncertainty-Aware Data Aggregation for Deep Imitation Learning, Y. Cui et al., ICRA 2019
Goal-conditioned Imitation Learning, Y. Ding et al., ICML Workshop 2019
Adversarial Imitation Learning from Incomplete Demonstrations, M. Sun et al., 2019
Generative Adversarial Self-Imitation Learning, J. Oh et al., 2019
Wasserstein Adversarial Imitation Learning, H. Xiao et al., 2019
Learning Plannable Representations with Causal InfoGAN, T. Kurutach et al., NeurIPS 2018
Self-Imitation Learning, J. Oh et al., ICML 2018
Deep Q-learning from Demonstrations, T. Hester et al., AAAI 2018
An Algorithmic Perspective on Imitation Learning, T. Osa et al., 2018
Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning, I. Kostrikov et al., 2018
Universal Planning Networks, A. Srinivas et al., 2018
Learning to Search via Retrospective Imitation, J. Song et al., 2018
Third-Person Imitation Learning, B. Stadie et al., ICLR 2017
RAIL: Risk-Averse Imitation Learning, A. Santara et al., NIPS 2017
Generative Adversarial Imitation Learning, J. Ho et al., NIPS 2016
Model Imitation for Model-Based Reinforcement Learning, Y. Wu et al., 2019
Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations, D. Brown et al., CoRL 2019
Task-Relevant Adversarial Imitation Learning, K. Zolna et al., 2019
Multi-Task Hierarchical Imitation Learning for Home Automation, R. Fox et al., 2019
Imitation Learning for Human Pose Prediction, B. Wang et al., 2019
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems, C. Gulcehre et al., 2019
Imitation Learning from Video by Leveraging Proprioception, F. Torabi et al., IJCAI 2019
Adversarial Imitation Learning from Incomplete Demonstrations, M. Sun et al., 2019
End-to-end Driving via Conditional Imitation Learning, F. Codevilla et al., ICRA 2018
R2P2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting, N. Rhinehart et al., ECCV 2018 [blog]
End-to-End Learning Driver Policy using Moments Deep Neural Network, D. Qian et al., ROBIO 2018
Learning Montezuma’s Revenge from a Single Demonstration, T. Salimans., et al., 2018
ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst, M. Bansal et al., 2018
Video Imitation GAN: Learning control policies by imitating raw videos using generative adversarial reward estimation, S. Chaudhury et al., 2018
Query-Efficient Imitation Learning for End-to-End Autonomous Driving, J. Zhang et al., 2016
Imitation Learning: Progress, Taxonomies and Challenges, Zheng et al., 2022
Deep Reinforcement Learning: An Overview, Y. Li, 2018
A Brief Survey of Deep Reinforcement Learning, K. Arulkumaran et al., 2017
Imitation Learning : A Survey of Learning Methods, A. Hussein et al.
Graph-Structured Visual Imitation, M. Sieb et al., CoRL 2019
On-Policy Robot Imitation Learning from a Converging Supervisor, A. Balakrishna et al., CoRL 2019
Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Reward, M. Vecerik et al., 2017
Zero-Shot Visual Imitation, D. Pathak et al., ICLR 2018
One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks, T. Yu et al., 2018
One-Shot Imitation Learning, Y. Duan et al., NIPS 2017
Learning a Multi-Modal Policy via Imitating Demonstrations with Mixed Behaviors, F Hsiao et al., 2019
Watch, Try, Learn: Meta-Learning from Demonstrations and Reward. Imitation learning, A. Zhou et al., 2019
Shared Multi-Task Imitation Learning for Indoor Self-Navigation, J. Xu et al., 2018
Robust Imitation of Diverse Behaviors, Z. Wang et al., NIPS 2017
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets, K. Hausman et al., NIPS 2017
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations, Y. Li et al., NIPS 2017
Learning Compound Tasks without Task-specific Knowledge via Imitation and Self-supervised Learning, S. Lee et al., ICML 2020
CompILE: Compositional Imitation Learning and Execution, T. Kipf et al., ICML 2019
Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information, M. Sharma et al., ICLR 2019
Hierarchical Imitation and Reinforcement Learning, H. Le et al., ICML 2018
OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning, P. Henderson et al., AAAI 2018
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences, D. Brown et al., ICML 2020
A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents, Y. Wu et al., AAAI 2018
Deep Reinforcement Learning from Human Preferences, P. Christiano et al., NIPS 2017
Self-Supervised Adversarial Imitation Learning M. Juarez et al., IJCNN 2023
MobILE: Model-Based Imitation Learning From Observation Alone, Kidambi et al., NeurIPS 2021
Off-Policy Imitation Learning from Observations, Zhu et al., NeurIPS 2020
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement, C. Yang et al., NeurIPS 2019
To Follow or not to Follow: Selective Imitation Learning from Observations, Y. Lee et al., CoRL 2019
Provably Efficient Imitation Learning from Observation Alone, W. Sun et al., ICML 2019
To follow or not to follow: Selective Imitation Learning from Observations, Y. Lee et al.
Recent Advances in Imitation Learning from Observation, F. Torabi et al., IJCAI 2019
Adversarial Imitation Learning from State-only Demonstrations, F. Torabi et al., AAMAS 2019
Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation, Y. Liu et al., 2018
Observational Learning by Reinforcement Learning, D. Borsa et al., 2017
Safe end-to-end imitation learning for model predictive control, K. Lee et al., ICRA 2019
Deep Imitative Models for Flexible Inference, Planning, and Control, N. Rhinehart et al., 2019 [blog]
Model-based imitation learning from state trajectories, S. Chaudhury et al., 2018
End-to-End Differentiable Adversarial Imitation Learning, N. Baram et al., ICML 2017
Imitating Unknown Policies via Exploration, G. Nathan et al., BMVC 2020
Augmented Behavioral Cloning from Observation, M. Juarez et al., IJCNN 2020
Truly Batch Apprenticeship Learning with Deep Successor Features, D. Lee et al., 2019
SQIL: Imitation Learning via Regularized Behavioral Cloning, S. Reddy et al., 2019
Behavioral Cloning from Observation, F. Torabi et al., IJCAI 2018
Causal Confusion in Imitation Learning, P. Haan et al., NeurIPS 2018
Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning, A. Gupta et al., CoRL 2019
Integration of Imitation Learning using GAIL and Reinforcement Learning using Task-achievement Rewards via Probabilistic Generative Model, A. Kinose et al., 2019
Reinforced Imitation in Heterogeneous Action Space, K. Zolna et al., 2019
Reinforcement and Imitation Learning for Diverse Visuomotor Skills, Y. Zhu et al., RSS 2018
Policy Optimization with Demonstrations, B. Kang et al., ICML 2018
Reinforcement Learning from Imperfect Demonstrations, Y. Gao et al., ICML Workshop 2018
Pre-training with Non-expert Human Demonstration for Deep Reinforcement Learning, G. Cruz Jr et al., 2018
Sparse Reward Based Manipulator Motion Planning by Using High Speed Learning from Demonstrations, G. Zuo et al., ROBIO 2018
Independent Generative Adversarial Self-Imitation Learning in Cooperative Multiagent Systems, X. Hao et al., AAMAS 2019
PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings, N. Rhinehart et al., 2019 [blog]
Intrinsic Reward Driven Imitation Learning via Generative Model, X. Yu et al., ICML 2020
Inferring Task Goals and Constraints using Bayesian Nonparametric Inverse Reinforcement Learning, D. Park et al., CoRL 2019
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations, D. Brown et al., ICML 2019
Learning Reward Functions by Integrating Human Demonstrations and Preferences, M. Palan et al., 2019
Learning Robust Rewards with Adversarial Inverse Reinforcement Learning, J. Fu et al., 2018
Model-Free Deep Inverse Reinforcement Learning by Logistic Regression, E. Uchibe, 2018
Compatible Reward Inverse Reinforcement Learning, A. Metelli et al., NIPS 2017
A Connection Between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models, C. Finn et al., NIPS Workshop 2016
Maximum Entropy Inverse Reinforcement Learning, B. Ziebart et al., AAAI 2008
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