Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control

2026-08-07

AI Summary

In reinforcement learning, naively stacking algorithmic components often leads to performance degradation and non-stationarity. To address this issue, this work proposes the ROSER framework, which systematically investigates the synergistic mechanisms among model representation, optimization stability, and experience replay. The study reveals that the effectiveness of these interactions is highly task-dependent and establishes principled guidelines for their co-design. By jointly optimizing these dimensions, ROSER achieves substantial improvements over existing methods across multiple continuous control benchmarks, outperforming naive component stacking by an average of 17.60% in performance and significantly enhancing sample efficiency.

Abstract

Reinforcement learning systems are significantly more complex than other machine learning paradigms due to inherent properties, causing RL system design to jointly account for many tightly coupled factors. Despite advances in individual algorithmic components, their functional interdependencies remain underexplored: do they exhibit mutual synergy or counterproductive interference? To bridge this gap, we conduct a systematic investigation and find that the efficacy of different components exhibits significant task-dependency, and naively stacking state-of-the-art techniques does not necessarily yield performance gains; instead, it often triggers emergent challenges, such as compounded non-stationarity. Building upon these findings, we distill a suite of actionable insights into the principled coordination of these components. Guided by these insights, we propose ROSER, an RL framework that coordinates three critical dimensions: Model-based Representation, Optimization Stability, and Experience Replay. Across diverse continuous-control benchmarks, ROSER consistently outperforms vanilla baselines and achieves 17.60% gains over naive stack. Our findings underscore the necessity of a holistic perspective in RL system design and paves the way for developing sample-efficient agents.

Problem

• reinforcement learning
• component synergy
• sample efficiency
• continuous control
• non-stationarity

Innovation

• component synergy
• sample-efficient reinforcement learning
• non-stationarity
• model-based representation
• experience replay