UNIQ: Offline Inverse Q-learning for Avoiding Undesirable Demonstrations

We address the problem of offline learning a policy that avoids undesirable demonstrations. Unlike conventional offline imitation learning approaches that aim to imitate expert or near-optimal demonstrations, our setting involves avoiding undesirable behavior (specified using undesirable demonstrations). To tackle this problem, unlike standard imitation learning where the aim is to minimize the distance between learning policy and expert demonstrations, we formulate the learning task as maximizing a statistical distance, in the space of state-action stationary distributions, between the learning policy and the undesirable policy. This significantly different approach results in a novel training objective that necessitates a new algorithm to address it. Our algorithm, UNIQ, tackles these challenges by building on the inverse Q-learning framework, framing the learning problem as a cooperative (non-adversarial) task. We then demonstrate how to efficiently leverage unlabeled data for practical training. Our method is evaluated on standard benchmark environments, where it consistently outperforms state-of-the-art baselines. The code implementation can be accessed at: https://github.com/hmhuy0/UNIQ. Reinforcement learning (RL) is a powerful framework for learning to maximize expected returns and has achieved remarkable success across various domains. However, applying reinforcement learning to real-world problems is challenging due to difficulties in designing reward functions and the requirement for extensive online interactions with the environment. While some approaches have addressed these challenges, they often rely on costly datasets, requiring either accurate labeling or clean, consistent data, which is often impractical. Imitation learning (Abbeel & Ng, 2004; Ziebart et al., 2008; Kelly et al., 2019) offers a more feasible alternative, enabling agents to learn directly from expert demonstrations without the need for explicit reward signals.