mlsanityData to 7 Oct 2026

Regularized Inverse Reinforcement Learning

arXiv 2010.03691 ↗First posted 7 Oct 2020cs.LGcited by 9

On the map: Offline RL and imitation learning from data → Inverse reinforcement learning

Abstract (arXiv)

Inverse Reinforcement Learning (IRL) aims to facilitate a learner's ability to imitate expert behavior by acquiring reward functions that explain the expert's decisions. Regularized IRL applies strongly convex regularizers to the learner's policy in order to avoid the expert's behavior being rationalized by arbitrary constant rewards, also known as degenerate solutions. We propose tractable solutions, and practical methods to obtain them, for regularized IRL. Current methods are restricted to the maximum-entropy IRL framework, limiting them to Shannon-entropy regularizers, as well as proposing the solutions that are intractable in practice. We present theoretical backing for our proposed IRL method's applicability for both discrete and continuous controls, empirically validating our performance on a variety of tasks.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 21 of its 41 references that are in the map. First posted on arXiv 7 Oct 2020.

Builds on

21 of 41 references.

Closest ancestors · 1

Same direction, newest first

  1. 2018Maximum Causal Tsallis Entropy Imitation Learning22 May

Other references · 20

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2015Trust Region Policy Optimizationcited by 2,503
  4. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  5. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  6. 2016Generative Adversarial Imitation Learningcited by 1,576
  7. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 485
  8. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 400
  9. 2019A Theory of Regularized Markov Decision Processescited by 279
  10. 2019A Divergence Minimization Perspective on Imitation Learning Methodscited by 188
  11. 2016A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Modelscited by 167
  12. 2020GenDICE: Generalized Offline Estimation of Stationary Valuescited by 122
8 more
  1. 2019Imitation Learning as f-Divergence Minimizationcited by 121
  2. 2020Primal Wasserstein Imitation Learningcited by 120
  3. 2017Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networkscited by 107
  4. 2019rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorchcited by 38
  5. 2020Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Drivingcited by 34
  6. 2019Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learningcited by 18
  7. 2016Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networkscited by 15
  8. 2017Ambiguity set and learning via Bregman and Wassersteincited by 4

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 2; 2025: 2.

9 papers.

  1. 2025Reward Models in Deep Reinforcement Learning: A Surveycited by 24
  2. 2023Identifiability and Generalizability in Constrained Inverse Reinforcement Learningcited by 16
  3. 2021Offline Inverse Reinforcement Learningcited by 14
  4. 2024Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learningcited by 6
  5. 2022Robust Imitation via Mirror Descent Inverse Reinforcement Learningcited by 5
  6. 2023Curricular Subgoals for Inverse Reinforcement Learningcited by 5
  7. 2021A Generalised Inverse Reinforcement Learning Frameworkcited by 2
  8. 2025PersonaAgent with GraphRAG: Community-Aware Knowledge Graphs for Personalized LLMcited by 2
  9. 2024Multi Task Inverse Reinforcement Learning for Common Sense Rewardcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Convergence of a model-free entropy-regularized inverse reinforcement learning algorithm
  2. 2025Statistical analysis of Inverse Entropy-regularized Reinforcement Learning
  3. 2023Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
  4. 2023BC-IRL: Learning Generalizable Reward Functions from Demonstrations
  5. 2025Inverse Reinforcement Learning via Convex Optimization
  6. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
  7. 2024Inverse Reinforcement Learning with Sub-optimal Experts
  8. 2020Revisiting Maximum Entropy Inverse Reinforcement Learning: New Perspectives and Algorithms