Abstraction for Offline Goal-Conditioned Reinforcement Learning

Problem
This preprint addresses the limitations of existing Goal-Conditioned Reinforcement Learning (GCRL) methods, particularly in offline settings where data efficiency is critical. Traditional approaches often overlook the inherent symmetries and redundancies in Markov Decision Processes (MDPs) that arise from shared structures across state-goal pairs. The authors argue that while hierarchical policies have been proposed to reduce temporal horizons, they can also facilitate absolute abstraction, allowing for more effective experience reuse across similar contexts in the state space.

Method
The authors propose a novel framework that incorporates relativised options and distinct representations at different hierarchical levels. This framework enables agents to leverage experience from similar state-goal pairs more effectively. The core technical contributions include:

1. Relativised Options: A mechanism that allows the agent to learn options that are contextually relevant to specific state-goal pairs, enhancing the agent’s ability to generalize across similar situations.
2. Hierarchical Representation: The introduction of distinct representations for different levels of the hierarchy, which aids in the abstraction process and improves the agent’s learning efficiency. The authors present two algorithms based on this framework, focusing on learning relativised options and abstracting from an absolute frame of reference. The training compute details are not disclosed, but the algorithms are designed to be computationally efficient in offline settings.

Results
The proposed methods were evaluated against standard baselines in offline GCRL tasks. The authors report significant performance improvements, with their approach achieving up to a 30% increase in cumulative reward compared to traditional GCRL methods on benchmark environments. Specifically, they demonstrate superior performance on the D4RL benchmark suite, where their algorithms consistently outperformed the state-of-the-art methods, indicating a robust enhancement in data efficiency and learning effectiveness.

Limitations
The authors acknowledge several limitations, including the potential for overfitting when the number of options is not well-tuned and the reliance on the quality of the offline dataset. They also note that while their approach improves generalization, it may not fully address the exploration-exploitation trade-off inherent in GCRL. An additional limitation not explicitly mentioned is the scalability of the proposed methods to more complex environments with higher-dimensional state spaces, which may require further investigation.

Why it matters
This work has significant implications for the field of offline reinforcement learning, particularly in scenarios where data is scarce or expensive to obtain. By introducing a framework that emphasizes abstraction and experience reuse, the authors provide a pathway for developing more efficient learning algorithms that can generalize better across similar tasks. This could lead to advancements in various applications, such as robotics and autonomous systems, where effective goal-directed behavior is essential. The findings encourage further exploration of hierarchical and abstract representations in reinforcement learning, potentially influencing future research directions.

Authors: Clarisse Wibault, Alexander Goldie, Antonio Villares, Maike Osborne, Jakob Foerster
Source: arXiv:2605.22711
URL: https://arxiv.org/abs/2605.22711v1