mlsanityData to 14 Oct 2026

Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets

arXiv 2005.10622 ↗First posted 19 May 2020cs.LG cs.AI cs.ROcited by 11

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

Abstract (arXiv)

Generative adversarial imitation learning (GAIL) has shown promising results by taking advantage of generative adversarial nets, especially in the field of robot learning. However, the requirement of isolated single modal demonstrations limits the scalability of the approach to real world scenarios such as autonomous vehicles' demand for a proper understanding of human drivers' behavior. In this paper, we propose a novel multi-modal GAIL framework, named Triple-GAIL, that is able to learn skill selection and imitation jointly from both expert demonstrations and continuously generated experiences with data augmentation purpose by introducing an auxiliary skill selector. We provide theoretical guarantees on the convergence to optima for both of the generator and the selector respectively. Experiments on real driver trajectories and real-time strategy game datasets demonstrate that Triple-GAIL can better fit multi-modal behaviors close to the demonstrators and outperforms state-of-the-art methods.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 10 of its 24 references that are in the map. First posted on arXiv 19 May 2020.

Builds on

10 of 24 references.

Closest ancestors · 1

Same direction, newest first

  1. 2019SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards27 May

Other references · 9

  1. 2015Trust Region Policy Optimizationcited by 2,580
  2. 2016Generative Adversarial Imitation Learningcited by 1,597
  3. 2015Maximum Entropy Deep Inverse Reinforcement Learningcited by 176
  4. 2017Robust Imitation of Diverse Behaviorscited by 86
  5. 2017Learning human behaviors from motion capture by adversarial imitationcited by 77
  6. 2017Triple Generative Adversarial Netscited by 60
  7. 2019Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Trafficcited by 50
  8. 2017ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Gamescited by 17
  9. 2017Burn-In Demonstrations for Multi-Modal Imitation Learningcited by 9

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Cited by year

Citing papers per year: 2021: 4; 2022: 2; 2023: 1; 2024: 3; 2025: 1.

11 papers.

  1. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 61
  2. 2021Learning Multimodal Rewards from Rankingscited by 41
  3. 2021Adversarial Option-Aware Hierarchical Imitation Learningcited by 19
  4. 2022Diverse Imitation Learning via Self-Organizing Generative Modelscited by 5
  5. 2023Hierarchical Imitation Learning for Stochastic Environmentscited by 3
  6. 2021Sample Efficient Imitation Learning via Reward Function Trained in Advancecited by 1
  7. 2024PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimizationcited by 1
  8. 2024Learning Strategy Representation for Imitation Learning in Multi-Agent Gamescited by 1
  9. 2022A New Approach to Training Multiple Cooperative Agents for Autonomous Drivingcited by 0
  10. 2024MRIC: Model-Based Reinforcement-Imitation Learning with Mixture-of-Codebooks for Autonomous Driving Simulationcited by 0
  11. 2025Imitation Learning in the Deep Learning Era: A Novel Taxonomy and Recent Advancescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2018Multi-Agent Imitation Learning for Driving Simulation
  2. 2020Modeling Human Driving Behavior through Generative Adversarial Imitation Learning
  3. 2020On Computation and Generalization of Generative Adversarial Imitation Learning
  4. 2019Situated GAIL: Multitask imitation using task-conditioned adversarial inverse reinforcement learning
  5. 2023Exploring Gradient Explosion in Generative Adversarial Imitation Learning: A Probabilistic Perspective
  6. 2018Sample-Efficient Imitation Learning via Generative Adversarial Nets
  7. 2021Generative Adversarial Imitation Learning for End-to-End Autonomous Driving on Urban Environments
  8. 2021A Pragmatic Look at Deep Imitation Learning