Imitation Learning from Observation through Optimal Transport

• URL: http://arxiv.org/abs/2310.01632v2
• Date: Thu, 03 Oct 2024 19:13:51 GMT
• Title: Imitation Learning from Observation through Optimal Transport
• Authors: Wei-Di Chang, Scott Fujimoto, David Meger, Gregory Dudek,
• Abstract summary: Imitation Learning from Observation (ILfO) is a setting in which a learner tries to imitate the behavior of an expert. We show that existing methods can be simplified to generate a reward function without requiring learned models or adversarial learning. We demonstrate the effectiveness of this simple approach on a variety of continuous control tasks and find that it surpasses the state of the art in the IlfO setting.
• Score: 25.398983671932154
• License:
• Abstract: Imitation Learning from Observation (ILfO) is a setting in which a learner tries to imitate the behavior of an expert, using only observational data and without the direct guidance of demonstrated actions. In this paper, we re-examine optimal transport for IL, in which a reward is generated based on the Wasserstein distance between the state trajectories of the learner and expert. We show that existing methods can be simplified to generate a reward function without requiring learned models or adversarial learning. Unlike many other state-of-the-art methods, our approach can be integrated with any RL algorithm and is amenable to ILfO. We demonstrate the effectiveness of this simple approach on a variety of continuous control tasks and find that it surpasses the state of the art in the IlfO setting, achieving expert-level performance across a range of evaluation domains even when observing only a single expert trajectory without actions.

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