Enabling Off-Policy Imitation Learning with Deep Actor Critic Stabilization

• URL: http://arxiv.org/abs/2511.07288v1
• Date: Mon, 10 Nov 2025 16:35:50 GMT
• Title: Enabling Off-Policy Imitation Learning with Deep Actor Critic Stabilization
• Authors: Sayambhu Sen, Shalabh Bhatnagar,
• Abstract summary: We introduce an adversarial imitation learning algorithm that incorporates off-policy learning to improve sample efficiency.<n>We demonstrate a reduction in the samples required to robustly match expert behavior.
• Score: 6.65616155956618
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Learning complex policies with Reinforcement Learning (RL) is often hindered by instability and slow convergence, a problem exacerbated by the difficulty of reward engineering. Imitation Learning (IL) from expert demonstrations bypasses this reliance on rewards. However, state-of-the-art IL methods, exemplified by Generative Adversarial Imitation Learning (GAIL)Ho et. al, suffer from severe sample inefficiency. This is a direct consequence of their foundational on-policy algorithms, such as TRPO Schulman et.al. In this work, we introduce an adversarial imitation learning algorithm that incorporates off-policy learning to improve sample efficiency. By combining an off-policy framework with auxiliary techniques specifically, double Q network based stabilization and value learning without reward function inference we demonstrate a reduction in the samples required to robustly match expert behavior.

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