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

2026-06-15

AI Summary

This work addresses the significant performance degradation of reinforcement learning (RL) agents under distribution shifts arising from mismatches or dynamics between training and deployment environments, a challenge exacerbated by the lack of a systematic understanding of their causal origins. By modeling agent–environment interaction through the lens of partially observable Markov decision processes (POMDPs), the study decomposes the RL framework into causal components—states, observations, policies, rewards, and transitions—and, incorporating temporal boundaries of shift occurrence, offers the first unified characterization of distribution shifts grounded in causal mechanisms. It distinguishes between internal (agent-driven) and external (environment-driven) sources and introduces a novel taxonomy encompassing explicit, implicit, and hybrid shifts. This framework establishes a structured classification and evaluation system for distribution shifts, enabling systematic analysis and targeted improvements of RL robustness.

Abstract

Reinforcement learning (RL) systems often degrade when operating conditions differ from those previously encountered, reflecting distributional shifts in the underlying data-generating process. Such shifts may occur between training and evaluation, as in In-Distribution (ID) and Out-of-Distribution (OOD) generalization, or within non-stationary settings where environment dynamics evolve over time. However, the formal relationship between these views remains unclear, and existing work mainly focuses on mitigation rather than the causal origin of shift within the agent-environment interaction. This work develops a unified causal-origin taxonomy that characterizes sources of distributional shift in RL and relates ID/OOD generalization to non-stationary settings. We transfer the classical dataset-shift principle from supervised learning to RL by reformulating distributional shift in terms of the generative interaction process. Using a Partially Observable Markov Decision Process (POMDP), we decompose the interaction into structural components, including the state distribution, observation process, policy, reward, and transition dynamics, together with the shifted-time boundary. The proposed taxonomy distinguishes internal, agent-driven, and external, environment-driven, distributional shifts. The shifted-time boundary perspective further characterizes explicit, implicit, and hybrid shifts. This formulation unifies ID/OOD generalization and non-stationarity as structured changes in the underlying process. We also introduce an evaluation framework for measuring shift impact and adaptation through performance degradation and recovery metrics. By grounding distributional shift in the causal-origin structure of RL, this work supports systematic analysis of robustness under distributional shift.

Problem

• distributional shift
• reinforcement learning
• causal origin
• non-stationarity
• generalization

Innovation

• causal-origin taxonomy
• distributional shift
• reinforcement learning
• non-stationarity
• POMDP