A curated list of awesome imitation learning (including inverse reinforcement learning and behavior cloning) resources, inspired by awesome-php. See also Awesome-Model-Based-Reinforcement-Learning and Awesome-Batch-Reinforcement-Learning.
Please feel free to send me pull request or email (kriswu8021@gmail.com) to add links.
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Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate, Y. Zhang et al., ICML 2020
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Provable Representation Learning for Imitation Learning via Bi-level Optimization, S. Arora et al., ICML 2020
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Domain Adaptive Imitation Learning, K. Kim et al., ICML 2020
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Imitation Learning from Imperfect Demonstration, Y. Wu et al., ICML 2019
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A Divergence Minimization Perspective on Imitation Learning Methods, S. Ghasemipour et al., CoRL 2019
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VILD: Variational Imitation Learning with Diverse-quality Demonstrations, V. Tangkaratt et al., 2019
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Sample-Efficient Imitation Learning via Generative Adversarial Nets, L. Blonde et al., AISTATS 2019
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Sample Efficient Imitation Learning for Continuous Control, F. Sasaki et al., ICLR 2019
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Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation, R. Wang et al., ICML 2019
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Uncertainty-Aware Data Aggregation for Deep Imitation Learning, Y. Cui et al., ICRA 2019
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Goal-conditioned Imitation Learning, Y. Ding et al., ICML Workshop 2019
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Adversarial Imitation Learning from Incomplete Demonstrations, M. Sun et al., 2019
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Generative Adversarial Self-Imitation Learning, J. Oh et al., 2019
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Wasserstein Adversarial Imitation Learning, H. Xiao et al., 2019
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Learning Plannable Representations with Causal InfoGAN, T. Kurutach et al., NeurIPS 2018
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Self-Imitation Learning, J. Oh et al., ICML 2018
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Deep Q-learning from Demonstrations, T. Hester et al., AAAI 2018
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An Algorithmic Perspective on Imitation Learning, T. Osa et al., 2018
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Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning, I. Kostrikov et al., 2018
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Universal Planning Networks, A. Srinivas et al., 2018
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Learning to Search via Retrospective Imitation, J. Song et al., 2018
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Third-Person Imitation Learning, B. Stadie et al., ICLR 2017
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RAIL: Risk-Averse Imitation Learning, A. Santara et al., NIPS 2017
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Generative Adversarial Imitation Learning, J. Ho et al., NIPS 2016
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Model Imitation for Model-Based Reinforcement Learning, Y. Wu et al., 2019
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Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations, D. Brown et al., CoRL 2019
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Task-Relevant Adversarial Imitation Learning, K. Zolna et al., 2019
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Multi-Task Hierarchical Imitation Learning for Home Automation, R. Fox et al., 2019
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Imitation Learning for Human Pose Prediction, B. Wang et al., 2019
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Making Efficient Use of Demonstrations to Solve Hard Exploration Problems, C. Gulcehre et al., 2019
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Imitation Learning from Video by Leveraging Proprioception, F. Torabi et al., IJCAI 2019
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Adversarial Imitation Learning from Incomplete Demonstrations, M. Sun et al., 2019
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End-to-end Driving via Conditional Imitation Learning, F. Codevilla et al., ICRA 2018
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R2P2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting, N. Rhinehart et al., ECCV 2018 [blog]
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End-to-End Learning Driver Policy using Moments Deep Neural Network, D. Qian et al., ROBIO 2018
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Learning Montezuma’s Revenge from a Single Demonstration, T. Salimans., et al., 2018
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ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst, M. Bansal et al., 2018
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Video Imitation GAN: Learning control policies by imitating raw videos using generative adversarial reward estimation, S. Chaudhury et al., 2018
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Query-Efficient Imitation Learning for End-to-End Autonomous Driving, J. Zhang et al., 2016
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Deep Reinforcement Learning: An Overview, Y. Li, 2018
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A Brief Survey of Deep Reinforcement Learning, K. Arulkumaran et al., 2017
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Imitation Learning : A Survey of Learning Methods, A. Hussein et al.
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Graph-Structured Visual Imitation, M. Sieb et al., CoRL 2019
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On-Policy Robot Imitation Learning from a Converging Supervisor, A. Balakrishna et al., CoRL 2019
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Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Reward, M. Vecerik et al., 2017
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Zero-Shot Visual Imitation, D. Pathak et al., ICLR 2018
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One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks, T. Yu et al., 2018
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One-Shot Imitation Learning, Y. Duan et al., NIPS 2017
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Learning a Multi-Modal Policy via Imitating Demonstrations with Mixed Behaviors, F Hsiao et al., 2019
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Watch, Try, Learn: Meta-Learning from Demonstrations and Reward. Imitation learning, A. Zhou et al., 2019
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Shared Multi-Task Imitation Learning for Indoor Self-Navigation, J. Xu et al., 2018
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Robust Imitation of Diverse Behaviors, Z. Wang et al., NIPS 2017
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Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets, K. Hausman et al., NIPS 2017
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InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations, Y. Li et al., NIPS 2017
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Learning Compound Tasks without Task-specific Knowledge via Imitation and Self-supervised Learning, S. Lee et al., ICML 2020
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CompILE: Compositional Imitation Learning and Execution, T. Kipf et al., ICML 2019
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Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information, M. Sharma et al., ICLR 2019
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Hierarchical Imitation and Reinforcement Learning, H. Le et al., ICML 2018
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OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning, P. Henderson et al., AAAI 2018
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Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences, D. Brown et al., ICML 2020
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A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents, Y. Wu et al., AAAI 2018
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Deep Reinforcement Learning from Human Preferences, P. Christiano et al., NIPS 2017
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Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement, C. Yang et al., NeurIPS 2019
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To Follow or not to Follow: Selective Imitation Learning from Observations, Y. Lee et al., CoRL 2019
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Provably Efficient Imitation Learning from Observation Alone, W. Sun et al., ICML 2019
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To follow or not to follow: Selective Imitation Learning from Observations, Y. Lee et al.
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Recent Advances in Imitation Learning from Observation, F. Torabi et al., IJCAI 2019
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Adversarial Imitation Learning from State-only Demonstrations, F. Torabi et al., AAMAS 2019
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Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation, Y. Liu et al., 2018
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Observational Learning by Reinforcement Learning, D. Borsa et al., 2017
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Safe end-to-end imitation learning for model predictive control, K. Lee et al., ICRA 2019
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Deep Imitative Models for Flexible Inference, Planning, and Control, N. Rhinehart et al., 2019 [blog]
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Model-based imitation learning from state trajectories, S. Chaudhury et al., 2018
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End-to-End Differentiable Adversarial Imitation Learning, N. Baram et al., ICML 2017
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Imitating Unknown Policies via Exploration, G. Nathan et al., BMVC 2020
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Augmented Behavioral Cloning from Observation, M. Juarez et al., IJCNN 2020
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Truly Batch Apprenticeship Learning with Deep Successor Features, D. Lee et al., 2019
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SQIL: Imitation Learning via Regularized Behavioral Cloning, S. Reddy et al., 2019
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Behavioral Cloning from Observation, F. Torabi et al., IJCAI 2018
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Causal Confusion in Imitation Learning, P. Haan et al., NeurIPS 2018
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Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning, A. Gupta et al., CoRL 2019
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Integration of Imitation Learning using GAIL and Reinforcement Learning using Task-achievement Rewards via Probabilistic Generative Model, A. Kinose et al., 2019
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Reinforced Imitation in Heterogeneous Action Space, K. Zolna et al., 2019
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Reinforcement and Imitation Learning for Diverse Visuomotor Skills, Y. Zhu et al., RSS 2018
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Policy Optimization with Demonstrations, B. Kang et al., ICML 2018
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Reinforcement Learning from Imperfect Demonstrations, Y. Gao et al., ICML Workshop 2018
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Pre-training with Non-expert Human Demonstration for Deep Reinforcement Learning, G. Cruz Jr et al., 2018
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Sparse Reward Based Manipulator Motion Planning by Using High Speed Learning from Demonstrations, G. Zuo et al., ROBIO 2018
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Independent Generative Adversarial Self-Imitation Learning in Cooperative Multiagent Systems, X. Hao et al., AAMAS 2019
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PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings, N. Rhinehart et al., 2019 [blog]
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Intrinsic Reward Driven Imitation Learning via Generative Model, X. Yu et al., ICML 2020
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Inferring Task Goals and Constraints using Bayesian Nonparametric Inverse Reinforcement Learning, D. Park et al., CoRL 2019
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Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations, D. Brown et al., ICML 2019
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Learning Reward Functions by Integrating Human Demonstrations and Preferences, M. Palan et al., 2019
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Learning Robust Rewards with Adversarial Inverse Reinforcement Learning, J. Fu et al., 2018
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Model-Free Deep Inverse Reinforcement Learning by Logistic Regression, E. Uchibe, 2018
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Compatible Reward Inverse Reinforcement Learning, A. Metelli et al., NIPS 2017
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A Connection Between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models, C. Finn et al., NIPS Workshop 2016
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Maximum Entropy Inverse Reinforcement Learning, B. Ziebart et al., AAAI 2008
- Learning Belief Representations for Imitation Learning in POMDPs, T. Gangwani et al., 2019
- Dyna-AIL : Adversarial Imitation Learning by Planning, V. Saxena et al., 2019
- 2018 ICML (Slides)
- Imitation learning basic (National Taiwan University)
- New Frontiers in Imitation Learning (2017)
- Unity Course
- Imitation Learning
- CMU Imitation Learning
- Deep Reinforcement Learning via Imitation Learning, S. Levine
License
To the extent possible under law, Yueh-Hua Wu has waived all copyright and related or neighboring rights to this work.