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[2602.18724] Task-Aware Exploration via a Predictive Bisimulation Metric
Summary
The paper presents TEB, a Task-aware Exploration approach that enhances exploration in visual reinforcement learning by utilizing a predictive bisimulation metric to couple task-relevant representations with exploration strategies.
Why It Matters
This research addresses the challenge of sparse rewards in visual reinforcement learning, proposing a novel method that improves exploration efficiency. By integrating task-aware metrics, it has the potential to advance AI applications in complex environments, making it significant for researchers and practitioners in the field.
Key Takeaways
- TEB improves exploration in visual reinforcement learning under sparse rewards.
- It utilizes a predictive bisimulation metric to enhance task-relevant representation.
- The method mitigates representation collapse, leading to better exploration strategies.
- Extensive experiments demonstrate TEB's superiority over existing baselines.
- This approach can significantly impact AI applications in complex environments.
Computer Science > Artificial Intelligence arXiv:2602.18724 (cs) [Submitted on 21 Feb 2026] Title:Task-Aware Exploration via a Predictive Bisimulation Metric Authors:Dayang Liang, Ruihan Liu, Lipeng Wan, Yunlong Liu, Bo An View a PDF of the paper titled Task-Aware Exploration via a Predictive Bisimulation Metric, by Dayang Liang and 3 other authors View PDF HTML (experimental) Abstract:Accelerating exploration in visual reinforcement learning under sparse rewards remains challenging due to the substantial task-irrelevant variations. Despite advances in intrinsic exploration, many methods either assume access to low-dimensional states or lack task-aware exploration strategies, thereby rendering them fragile in visual domains. To bridge this gap, we present TEB, a Task-aware Exploration approach that tightly couples task-relevant representations with exploration through a predictive Bisimulation metric. Specifically, TEB leverages the metric not only to learn behaviorally grounded task representations but also to measure behaviorally intrinsic novelty over the learned latent space. To realize this, we first theoretically mitigate the representation collapse of degenerate bisimulation metrics under sparse rewards by internally introducing a simple but effective predicted reward differential. Building on this robust metric, we design potential-based exploration bonuses, which measure the relative novelty of adjacent observations over the latent space. Extensive experiments on ...
