mlsanityData to 7 Oct 2026

Maximum Entropy Deep Inverse Reinforcement Learning

arXiv 1507.04888 ↗First posted 17 Jul 2015cs.LGcited by 173

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Cited by 91 in Offline RL and imitation learning from data, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

This paper presents a general framework for exploiting the representational capacity of neural networks to approximate complex, nonlinear reward functions in the context of solving the inverse reinforcement learning (IRL) problem. We show in this context that the Maximum Entropy paradigm for IRL lends itself naturally to the efficient training of deep architectures. At test time, the approach leads to a computational complexity independent of the number of demonstrations, which makes it especially well-suited for applications in life-long learning scenarios. Our approach achieves performance commensurate to the state-of-the-art on existing benchmarks while exceeding on an alternative benchmark based on highly varying reward structures. Finally, we extend the basic architecture - which is equivalent to a simplified subclass of Fully Convolutional Neural Networks (FCNNs) with width one - to include larger convolutions in order to eliminate dependency on precomputed spatial features and work on raw input representations.

Key facts. Cited by 173 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jul 2015.

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Citing papers per year: 2018: 5; 2019: 13; 2020: 16; 2021: 25; 2022: 24; 2023: 28; 2024: 31; 2025: 19; 2026: 12.

173 papers.

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  1. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 294
  2. 2019Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observationscited by 256
  3. 2021End-to-end Interpretable Neural Motion Plannercited by 240
  4. 2026Self-Distillation Enables Continual Learningcited by 212
  5. 2020Language Conditioned Imitation Learning over Unstructured Datacited by 208
  6. 2024Behavior Generation with Latent Actionscited by 159
  7. 2023A Survey of Imitation Learning: Algorithms, Recent Developments, and Challengescited by 145
  8. 2019Imitation Learning as f-Divergence Minimizationcited by 121
  9. 2021Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videoscited by 116
  10. 2021Dueling RL: Reinforcement Learning with Trajectory Preferencescited by 105
  11. 2024Towards learning-based planning:The nuPlan benchmark for real-world autonomous drivingcited by 103
  12. 2021Reward (Mis)design for Autonomous Drivingcited by 102
38 more
  1. 2024Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Reviewcited by 92
  2. 2021SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policiescited by 68
  3. 2020f-IRL: Inverse Reinforcement Learning via State Marginal Matchingcited by 67
  4. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 59
  5. 2020Predicting Goal-directed Human Attention Using Inverse Reinforcement Learningcited by 53
  6. 2022Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guaranteescited by 42
  7. 2021Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classificationcited by 38
  8. 2023PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedbackcited by 38
  9. 2023Learning Zero-Shot Cooperation with Humans, Assuming Humans Are Biasedcited by 37
  10. 2018Learning a Prior over Intent via Meta-Inverse Reinforcement Learningcited by 36
  11. 2021Lifelong Robotic Reinforcement Learning by Retaining Experiencescited by 34
  12. 2024Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applicationscited by 34
  13. 2020Robust Inverse Reinforcement Learning under Transition Dynamics Mismatchcited by 33
  14. 2019MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learningcited by 29
  15. 2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Reviewcited by 29
  16. 2024A Large Language Model-Driven Reward Design Framework via Dynamic Feedback for Reinforcement Learningcited by 29
  17. 2019SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewardscited by 28
  18. 2018Inverse reinforcement learning for video gamescited by 26
  19. 2022The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Typescited by 26
  20. 2023When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learningcited by 26
  21. 2022On the Sensitivity of Reward Inference to Misspecified Human Modelscited by 25
  22. 2019Interactive Teaching Algorithms for Inverse Reinforcement Learningcited by 24
  23. 2021PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learningcited by 23
  24. 2023Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directionscited by 23
  25. 2024RoadRunner -- Learning Traversability Estimation for Autonomous Off-road Drivingcited by 23
  26. 2020Scalable Multi-Task Imitation Learning with Autonomous Improvementcited by 21
  27. 2023Coherent Soft Imitation Learningcited by 21
  28. 2021Domain-Robust Visual Imitation Learning with Mutual Information Constraintscited by 20
  29. 2022Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidancecited by 20
  30. 2020Sequential Anomaly Detection using Inverse Reinforcement Learningcited by 19
  31. 2022End-to-End Stochastic Optimization with Energy-Based Modelcited by 19
  32. 2022Driving in Real Life with Inverse Reinforcement Learningcited by 18
  33. 2024S2AC: Energy-Based Reinforcement Learning with Stein Soft Actor Criticcited by 18
  34. 2020Active Task-Inference-Guided Deep Inverse Reinforcement Learningcited by 17
  35. 2020Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimizationcited by 16
  36. 2023Rating-based Reinforcement Learningcited by 14
  37. 2019Off-road Autonomous Vehicles Traversability Analysis and Trajectory Planning Based on Deep Inverse Reinforcement Learningcited by 13
  38. 2022A Primer on Maximum Causal Entropy Inverse Reinforcement Learningcited by 13

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