Latent Wasserstein Adversarial Imitation Learning

• URL: http://arxiv.org/abs/2603.05440v1
• Date: Thu, 05 Mar 2026 18:01:49 GMT
• Title: Latent Wasserstein Adversarial Imitation Learning
• Authors: Siqi Yang, Kai Yan, Alexander G. Schwing, Yu-Xiong Wang,
• Abstract summary: Imitation Learning (IL) enables agents to mimic expert behavior by learning from demonstrations.<n>We propose Latent Wasserstein Adrial Imitation Learning (LWAIL), a novel adversarial imitation learning framework.<n>We show that our method outperforms prior Wasserstein-based IL methods and prior adversarial IL methods.
• Score: 110.12916356445908
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Imitation Learning (IL) enables agents to mimic expert behavior by learning from demonstrations. However, traditional IL methods require large amounts of medium-to-high-quality demonstrations as well as actions of expert demonstrations, both of which are often unavailable. To reduce this need, we propose Latent Wasserstein Adversarial Imitation Learning (LWAIL), a novel adversarial imitation learning framework that focuses on state-only distribution matching. It benefits from the Wasserstein distance computed in a dynamics-aware latent space. This dynamics-aware latent space differs from prior work and is obtained via a pre-training stage, where we train the Intention Conditioned Value Function (ICVF) to capture a dynamics-aware structure of the state space using a small set of randomly generated state-only data. We show that this enhances the policy's understanding of state transitions, enabling the learning process to use only one or a few state-only expert episodes to achieve expert-level performance. Through experiments on multiple MuJoCo environments, we demonstrate that our method outperforms prior Wasserstein-based IL methods and prior adversarial IL methods, achieving better results across various tasks.

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