Adversarial Imitation Learning from Incomplete Demonstrations

Mingfei Sun, Xiaojuan Ma

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Imitation learning targets deriving a mapping from states to actions, a.k.a. policy, from expert demonstrations. Existing methods for imitation learning typically require any actions in the demonstrations to be fully available, which is hard to ensure in real applications. Though algorithms for learning with unobservable actions have been proposed, they focus solely on state information and overlook the fact that the action sequence could still be partially available and provide useful information for policy deriving. In this paper, we propose a novel algorithm called Action-Guided Adversarial Imitation Learning (AGAIL) that learns a policy from demonstrations with incomplete action sequences, i.e., incomplete demonstrations. The core idea of AGAIL is to separate demonstrations into state and action trajectories, and train a policy with state trajectories while using actions as auxiliary information to guide the training whenever applicable. Built upon the Generative Adversarial Imitation Learning, AGAIL has three components: a generator, a discriminator, and a guide. The generator learns a policy with rewards provided by the discriminator, which tries to distinguish state distributions between demonstrations and samples generated by the policy. The guide provides additional rewards to the generator when demonstrated actions for specific states are available. We compare AGAIL to other methods on benchmark tasks and show that AGAIL consistently delivers comparable performance to the state-of-the-art methods even when the action sequence in demonstrations is only partially available.
Original languageEnglish
Title of host publicationProceedings of the 28th International Joint Conference on Artificial Intelligence
EditorsSarit Kraus
Place of PublicationWashington DC
PublisherAAAI Press
Number of pages7
ISBN (Print)9780999241141
Publication statusPublished - 10 Aug 2019


  • Machine Learning: Reinforcement Learning
  • Behavior and Control
  • Learning in Robotics
  • Adversarial Machine Learning


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