In the past decade, reinforcement learning research has been at the forefront of scientific progress, particularly with the application of deep neural networks to approximate the state-action value function, which has led to victories in challenging games. This paper focuses on the key ingredients of this research and analyzes the canonical evaluation and design paradigms in reinforcement learning.
We introduce the theoretical foundations of scaling laws in reinforcement learning and demonstrate that there is no monotonic relationship between the asymptotic performance of reinforcement learning algorithms and their performance rankings or data regimes. Through large-scale experiments, our results indicate that a line of reinforcement learning research under these traditional paradigms has resulted in incorrect conclusions.
Our analysis and findings provide core insights into the scaling, capacity, and complexity of deep reinforcement learning.
Blogger's Review: This paper offers a profound exploration of evaluation and design methodologies in deep reinforcement learning, revealing potentially misleading conclusions from traditional research paradigms. For researchers, grasping these theoretical foundations and experimental outcomes is crucial for advancing the field.