SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning
Hojoon Lee Dongyoon Hwang Donghu Kim Hyunseung Kim Jun Jet Tai Kaushik Subramanian Peter R. Wurman Jaegul Choo Peter Stone Takuma Seno
Recent advances in CV and NLP have been largely driven by scaling up the number of network parameters, despite traditional theories suggesting that larger networks are prone to overfitting.
These large networks avoid overfitting by integrating components that induce a simplicity bias, guiding models toward simple and generalizable solutions. 
However, in deep RL, designing and scaling up networks have been less explored.
Motivated by this opportunity, we present SimBa, an architecture designed to scale up parameters in deep RL by injecting a simplicity bias. SimBa consists of three components: (i) an observation normalization layer that standardizes inputs with running statistics, (ii) a residual feedforward block to provide a linear pathway from the input to output, and (iii) a layer normalization to control feature magnitudes. 
By scaling up parameters with SimBa, the sample efficiency of various deep RL algorithms—including off-policy, on-policy, and unsupervised methods—is consistently improved.
Moreover, solely by integrating SimBa architecture into SAC, it matches or surpasses state-of-the-art deep RL methods with high computational efficiency across DMC, MyoSuite, and HumanoidBench.
These results demonstrate SimBa's broad applicability and effectiveness across diverse RL algorithms and environments.
reinforcement learning
In NLP and CV, scaling up model parameters improves performance, but in RL, scaling up often degrades it. This paper proposes a network architecture that allows RL algorithms to improve performance as model size increases.