Inverse Reinforcement Learning Reading List

Curated by Mouhssine Rifaki | Stanford Electrical Engineering | Last updated August 2026

Foundational and modern papers on inverse reinforcement learning — from maximum-entropy formulations to adversarial and Bayesian approaches for recovering reward functions from demonstrations.

Inverse Reinforcement Learning: 10 key papers

1. Maximum Entropy Deep Inverse Reinforcement Learning
   Wulfmeier et al. arXiv 2015.
2. Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
   Finn et al. arXiv 2016.
3. Learning Robust Rewards with Adversarial Inverse Reinforcement Learning
   Fu et al. arXiv 2017.
4. A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
   Arora and Doshi. arXiv 2018.
5. IQ-Learn: Inverse soft-Q Learning for Imitation
   Garg et al. arXiv 2021.
6. f-IRL: Inverse Reinforcement Learning via State Marginal Matching
   Ni et al. arXiv 2020.
7. On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference
   Shah et al. arXiv 2019.
8. Inverse Reinforcement Learning in Contextual MDPs
   Belogolovsky et al. arXiv 2019.
9. Learning Reward Functions by Integrating Human Demonstrations and Preferences
   Palan et al. arXiv 2019.
10. Inverse Reinforcement Learning without Reinforcement Learning
    Swamy et al. arXiv 2023.

Related reading lists

• Imitation Learning and Inverse RL
• Reward Learning & Reward Modeling
• Offline Imitation Learning
• RL from Human Feedback (RLHF)
• Adversarial Reinforcement Learning & Robustness to Adversaries
• Preference Learning & Learning from Comparisons
• Bayesian Optimization & Global Optimization
• Human-AI Collaboration & Teaming