Task-Aware Exploration via a Predictive Bisimulation Metric
arXiv:2602.18724v1 Announce Type: new 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
arXiv:2602.18724v1 Announce Type: new 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 MetaWorld and Maze2D show that TEB achieves superior exploration ability and outperforms recent baselines.
Executive Summary
This article presents a novel approach to task-aware exploration in visual reinforcement learning under sparse rewards, dubbed TEB (Task-aware Exploration via a Predictive Bisimulation Metric). TEB tightly couples task-relevant representations with exploration, leveraging a predictive Bisimulation metric to learn behaviorally grounded task representations and measure behaviorally intrinsic novelty. The authors theoretically mitigate the representation collapse of degenerate bisimulation metrics under sparse rewards by introducing a predicted reward differential. Extensive experiments on MetaWorld and Maze2D demonstrate TEB's superior exploration ability, outperforming recent baselines. This research fills a significant gap in visual reinforcement learning, offering a robust and efficient exploration strategy for real-world applications.
Key Points
- ▸ TEB is a novel task-aware exploration approach for visual reinforcement learning under sparse rewards.
- ▸ TEB tightly couples task-relevant representations with exploration through a predictive Bisimulation metric.
- ▸ The authors mitigate the representation collapse of degenerate bisimulation metrics under sparse rewards.
- ▸ TEB outperforms recent baselines in exploration ability on MetaWorld and Maze2D.
Merits
Strength
TEB's ability to learn behaviorally grounded task representations and measure behaviorally intrinsic novelty leads to superior exploration ability.
Demerits
Limitation
The introduction of a predicted reward differential may require significant computational resources, limiting its practical applicability.
Expert Commentary
The article makes a significant contribution to the field of visual reinforcement learning, addressing the long-standing challenge of sparse rewards. The authors' innovative approach, TEB, offers a robust and efficient exploration strategy, outperforming recent baselines. However, the introduction of a predicted reward differential may require significant computational resources, limiting its practical applicability. Future research should focus on mitigating this limitation while maintaining the benefits of TEB. The article's findings have far-reaching implications for the development of more robust and efficient reinforcement learning algorithms, potentially leading to significant breakthroughs in various fields.
Recommendations
- ✓ Future researchers should explore the application of TEB in real-world scenarios, such as robotics and autonomous systems, to evaluate its practical effectiveness.
- ✓ The authors should investigate the possibility of adapting TEB to other reinforcement learning domains, such as text-based games or financial markets.