Core Readings:
Andrew Ng and Stuart Russell, Algorithms for inverse reinforcement learning. ICML, 2000.
Pieter Abbeel and Andrew Ng, Apprenticeship learning via inverse reinforcement learning. ICML, 2004.
Deepak Ramachandran and E Amir, Bayesian Inverse Reinforcement Learning. IJCAI, 2007.
B. Ziebart, A. Maas, J. Bagnell, and A. Dey, Maximum entropy inverse reinforcement learning. AAAI, 2008.
Chelsea Finn, Paul Christiano, Pieter Abbeel, and Sergey Levine, A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models. NeurIPS Workshop on Adversarial Training, 2016.
Jonathan Ho and Stefano Ermon, Generative adversarial imitation learning. NeurIPS, 2016.
Yunzhu Li, Jiaming Song, and Stefano Ermon, InfoGAIL: Interpretable imitation learning from visual demonstrations. NeurIPS, 2017.
Additional Readings:
Ratliff, Zinkevich, Bagnell, Maximum Margin Planning, ICML 2006.
Kolter, Abbeel, Ng, Hierarchical Apprenticeship Learning, NeurIPS 2007.
Baker, Tenenbaum, Saxe, Goal Inference as Inverse Planning, Annual Meeting of the Cognitive Society, 2007.
Abbeel, Dolgov, Ng, Thrun, Apprenticeship Learning for Motion Planning with Application to Parking Lot Navigation, IROS 2008.
Mombaur, Laumond, Truong, An Inverse Optimal Control Approach to Human Modeling, ISSR 2009.
Finn, Abbeel, Levine, Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization, ICML 2016.
Fu, Luo, Levine, Learning Robust Rewards with Adversarial Inverse RL, ICLR 2018.