mlsanityData to 7 Oct 2026

Risk-Sensitive Generative Adversarial Imitation Learning

arXiv 1808.04468 ↗First posted 13 Aug 2018cs.LG cs.AI stat.MLcited by 16

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

We study risk-sensitive imitation learning where the agent's goal is to perform at least as well as the expert in terms of a risk profile. We first formulate our risk-sensitive imitation learning setting. We consider the generative adversarial approach to imitation learning (GAIL) and derive an optimization problem for our formulation, which we call it risk-sensitive GAIL (RS-GAIL). We then derive two different versions of our RS-GAIL optimization problem that aim at matching the risk profiles of the agent and the expert w.r.t. Jensen-Shannon (JS) divergence and Wasserstein distance, and develop risk-sensitive generative adversarial imitation learning algorithms based on these optimization problems. We evaluate the performance of our algorithms and compare them with GAIL and the risk-averse imitation learning (RAIL) algorithms in two MuJoCo and two OpenAI classical control tasks.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 10 of its 37 references that are in the map. First posted on arXiv 13 Aug 2018.

Builds on

10 of 37 references.

References · 10

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2015Trust Region Policy Optimizationcited by 2,503
  3. 2016Generative Adversarial Imitation Learningcited by 1,576
  4. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 400
  5. 2015Risk-Constrained Reinforcement Learning with Percentile Risk Criteriacited by 320
  6. 2014Optimizing the CVaR via Samplingcited by 98
  7. 2014Algorithms for CVaR Optimization in MDPscited by 98
  8. 2015Policy Gradient for Coherent Risk Measurescited by 74
  9. 2017Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methodscited by 12
  10. 2017RAIL: Risk-Averse Imitation Learningcited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2019: 1; 2020: 3; 2021: 3; 2022: 3; 2023: 1; 2024: 0; 2025: 4; 2026: 1.

16 papers.

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  1. 2020Primal Wasserstein Imitation Learningcited by 120
  2. 2020Safe Imitation Learning via Fast Bayesian Reward Inference from Preferencescited by 73
  3. 2021Risk-Averse Offline Reinforcement Learningcited by 65
  4. 2023Benchmarks and Algorithms for Offline Preference-Based Reward Learningcited by 55
  5. 2020Bayesian Robust Optimization for Imitation Learningcited by 28
  6. 2021Offline Preference-Based Apprenticeship Learningcited by 17
  7. 2021Policy Gradient Bayesian Robust Optimization for Imitation Learningcited by 11
  8. 2022On the convex formulations of robust Markov decision processescited by 8
  9. 2022Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planningcited by 3
  10. 2022Fail-Safe Adversarial Generative Imitation Learningcited by 2
  11. 2025TW-CRL: Time-Weighted Contrastive Reward Learning for Efficient Inverse Reinforcement Learningcited by 2
  12. 2019Relational Mimic for Visual Adversarial Imitation Learningcited by 0
4 more
  1. 2025Robustness in the Face of Partial Identifiability in Reward Learningcited by 0
  2. 2025TubeDAgger: Reducing the Number of Expert Interventions with Stochastic Reach-Tubescited by 0
  3. 2025Distributional Inverse Reinforcement Learningcited by 0
  4. 2026Strategically Robust Multi-Agent Reinforcement Learning with Linear Function Approximationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2020On Computation and Generalization of Generative Adversarial Imitation Learning
  2. 2023Exploring Gradient Explosion in Generative Adversarial Imitation Learning: A Probabilistic Perspective
  3. 2020Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate
  4. 2020f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning
  5. 2024C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory
  6. 2018Sample-Efficient Imitation Learning via Generative Adversarial Nets
  7. 2021Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximation
  8. 2020When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence