A Unified Causal-Origin Taxonomy of Distributional Shifts in Reinforcement Learning

Problem
Reinforcement learning (RL) systems often experience performance degradation when faced with distributional shifts, which can occur between training and evaluation phases or within non-stationary environments. Existing literature primarily addresses mitigation strategies without a comprehensive understanding of the causal origins of these shifts. This paper, being a preprint, aims to fill this gap by providing a unified causal-origin taxonomy that clarifies the relationship between in-distribution (ID) and out-of-distribution (OOD) generalization and non-stationary settings.

Method
The authors adapt the classical dataset-shift principle from supervised learning to the RL context by reformulating distributional shifts through the lens of the generative interaction process modeled as a Partially Observable Markov Decision Process (POMDP). They decompose the interaction into structural components: state distribution, observation process, policy, reward, and transition dynamics, while also introducing a shifted-time boundary. The taxonomy categorizes distributional shifts into internal (agent-driven) and external (environment-driven) shifts, further distinguishing between explicit, implicit, and hybrid shifts. An evaluation framework is proposed to measure the impact of these shifts on performance, utilizing metrics for degradation and recovery.

Results
The paper does not provide specific quantitative results or comparisons against established baselines, as it primarily focuses on theoretical contributions and the development of the taxonomy. However, the proposed framework is expected to facilitate future empirical evaluations of RL systems under various distributional shifts, potentially leading to improved robustness metrics.

Limitations
The authors acknowledge that their taxonomy is a conceptual framework and may require empirical validation through experiments in diverse RL environments. They do not address the computational complexity of implementing the proposed evaluation framework or the potential challenges in real-world applications where shifts may be more nuanced than the categories defined. Additionally, the lack of quantitative results in this preprint limits immediate applicability.

Why it matters
This work provides a foundational framework for understanding and analyzing distributional shifts in RL, which is critical for developing robust RL systems capable of adapting to changing environments. By linking ID/OOD generalization with non-stationarity, it opens avenues for future research aimed at enhancing the resilience of RL agents. The implications of this taxonomy extend to various applications, including robotics and autonomous systems, where adaptability to dynamic conditions is essential. This foundational work is crucial for advancing the field, as discussed in detail in the paper available on arXiv.