Mimicking Better by Matching the Approximate Action Distribution

• URL: http://arxiv.org/abs/2306.09805v3
• Date: Tue, 22 Oct 2024 11:33:36 GMT
• Title: Mimicking Better by Matching the Approximate Action Distribution
• Authors: João A. Cândido Ramos, Lionel Blondé, Naoya Takeishi, Alexandros Kalousis,
• Abstract summary: We introduce MAAD, a novel, sample-efficient on-policy algorithm for Imitation Learning from Observations. We show that it requires considerable fewer interactions to achieve expert performance, outperforming current state-of-the-art on-policy methods.
• Score: 48.95048003354255
• License:
• Abstract: In this paper, we introduce MAAD, a novel, sample-efficient on-policy algorithm for Imitation Learning from Observations. MAAD utilizes a surrogate reward signal, which can be derived from various sources such as adversarial games, trajectory matching objectives, or optimal transport criteria. To compensate for the non-availability of expert actions, we rely on an inverse dynamics model that infers plausible actions distribution given the expert's state-state transitions; we regularize the imitator's policy by aligning it to the inferred action distribution. MAAD leads to significantly improved sample efficiency and stability. We demonstrate its effectiveness in a number of MuJoCo environments, both int the OpenAI Gym and the DeepMind Control Suite. We show that it requires considerable fewer interactions to achieve expert performance, outperforming current state-of-the-art on-policy methods. Remarkably, MAAD often stands out as the sole method capable of attaining expert performance levels, underscoring its simplicity and efficacy.

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