Week 6: Inverse RL

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.