An awesome list of papers (eventually summaries too), code, videos and tutorials related to Inverse Reinforcement Learning.
- Algorithms for Inverse Reinforcement Learning - Andrew Ng & Stuart Russell (2000)
- Apprenticeship Learning via Inverse Reinforcement Learning - Pieter Abbeel & Andrew Ng (2004)
- Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods - Neu & Szepesvari (2007)
- Modeling Interaction via the Principle of Maximum Causal Entropy - Ziebart et. al. (2008)
- Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy - Brian Ziebart’s PhD Thesis (2010)
- Bayesian Inverse Reinforcement Learning - Deepak Ramachandran & Eyal Amir (2006)
- Relative Entropy Inverse Reinforcement Learning - Boularias et. al. (2011)
- Maximum Entropy Deep Inverse Reinforcement Learning - Wulfmeier et al. (2016)
- Hierarchical Bayesian Inverse Reinforcement Learning. Kim et al. (2012)
- Guided Cost Learning. Finn et al. (2016)
- Generative Adversarial Imitation Learning - Jonathan Ho and Stefano Ermon (2016)
- Occam’s razor is insufficient to infer the preferences of irrational agents - Stuart Armstrong and Sören Mindermann (2018)
- Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning - Daniel S. Brown et. al. (2019)
- On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference - Rohin Shah et. al. (2019)
- irl_rocks/IRL_survey.pdf
- A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress - Saurabh Arora and Prashant Doshi
- Inverse Reinforcement Learning Tutorial | part I | thinking wires - Johannes Heidecke
- Learning from humans: what is inverse reinforcement learning? - Jordan Alexander
- Deep RL Bootcamp Lecture 10B Inverse Reinforcement Learning - Chelsea Finn