ROSER Framework Boosts Sample Efficiency in Continuous Control RL

Reinforcement Learning (RL) systems are inherently complex, with numerous tightly coupled components whose interactions are often underexplored. While individual algorithmic advancements have been made, simply combining state-of-the-art techniques does not guarantee performance improvements; instead, it can lead to new challenges like compounded non-stationarity. This study systematically investigates these functional interdependencies, revealing that the effectiveness of different components is highly task-dependent. Building on these insights, the researchers distill actionable principles for coordinating RL components. Guided by these principles, they introduce ROSER, a novel RL framework designed to synergistically coordinate three critical dimensions: model-based representation, optimization stability, and experience replay. Across a diverse set of continuous-control benchmarks, ROSER consistently outperforms vanilla baselines and achieves substantial gains over a naive stacking of advanced techniques. The findings underscore the necessity of a holistic perspective in RL system design, paving the way for developing more sample-efficient agents.