Enabling Off-Policy Imitation Learning with Deep Actor Critic Stabilization

2025-11-10

AI Summary

Generative Adversarial Imitation Learning (GAIL) suffers from low sample efficiency and training instability due to its reliance on on-policy policy gradient methods (e.g., TRPO). Method: This paper proposes an off-policy adversarial imitation learning framework that integrates off-policy experience replay, twin Q-network stabilization, and value function learning—eliminating dependence on explicit reward functions and synchronized policy updates. Built upon a deep Actor-Critic architecture, it enables efficient policy learning without environmental reward signals. Contribution/Results: Experiments on multiple benchmark tasks demonstrate significant improvements in sample efficiency, with convergence accelerated by 3–5× compared to baseline methods. The framework also exhibits enhanced training stability and achieves expert-level performance using substantially fewer expert demonstrations.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Multiagent Systems: Adversarial Agents
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Improves sample efficiency in imitation learning using off-policy methods
• Stabilizes adversarial imitation learning with deep actor-critic techniques
• Reduces dependency on reward engineering through expert demonstration learning

Innovation

• Uses off-policy learning for sample efficiency
• Implements double Q network stabilization technique
• Learns value functions without reward inference