mlsanityData to 14 Oct 2026

Dyna-AIL : Adversarial Imitation Learning by Planning

arXiv 1903.03234 ↗First posted 8 Mar 2019cs.LG cs.AI stat.MLcited by 1

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

Abstract (arXiv)

Adversarial methods for imitation learning have been shown to perform well on various control tasks. However, they require a large number of environment interactions for convergence. In this paper, we propose an end-to-end differentiable adversarial imitation learning algorithm in a Dyna-like framework for switching between model-based planning and model-free learning from expert data. Our results on both discrete and continuous environments show that our approach of using model-based planning along with model-free learning converges to an optimal policy with fewer number of environment interactions in comparison to the state-of-the-art learning methods.

Key facts. Cited by 1 paper 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 8 of its 13 references that are in the map. First posted on arXiv 8 Mar 2019.

Builds on

8 of 13 references.

References · 8

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015Trust Region Policy Optimizationcited by 2,580
  3. 2016OpenAI Gymcited by 1,736
  4. 2016Generative Adversarial Imitation Learningcited by 1,597
  5. 2015Policy Distillationcited by 440
  6. 2015Learning Continuous Control Policies by Stochastic Value Gradientscited by 162
  7. 2018Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learningcited by 12
  8. 2017MBMF: Model-Based Priors for Model-Free Reinforcement Learningcited by 4

Built on by

Cited by year

Citing papers per year: 2022: 1.

1 paper.

  1. 2022Model-Based Imitation Learning Using Entropy Regularization of Model and Policycited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2023Provably Efficient Adversarial Imitation Learning with Unknown Transitions
  2. 2020ADAIL: Adaptive Adversarial Imitation Learning
  3. 2024Efficient Imitation Learning with Conservative World Models
  4. 2022Understanding Adversarial Imitation Learning in Small Sample Regime: A Stage-coupled Analysis
  5. 2025Model Predictive Adversarial Imitation Learning for Planning from Observation
  6. 2022Learning from Guided Play: Improving Exploration for Adversarial Imitation Learning with Simple Auxiliary Tasks
  7. 2022Planning for Sample Efficient Imitation Learning
  8. 2025SD2AIL: Adversarial Imitation Learning from Synthetic Demonstrations via Diffusion Models