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arxiv:2402.08609

Mixtures of Experts Unlock Parameter Scaling for Deep RL

Published on Feb 13
ยท Submitted by akhaliq on Feb 14
#3 Paper of the day
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Abstract

The recent rapid progress in (self) supervised learning models is in large part predicted by empirical scaling laws: a model's performance scales proportionally to its size. Analogous scaling laws remain elusive for reinforcement learning domains, however, where increasing the parameter count of a model often hurts its final performance. In this paper, we demonstrate that incorporating Mixture-of-Expert (MoE) modules, and in particular Soft MoEs (Puigcerver et al., 2023), into value-based networks results in more parameter-scalable models, evidenced by substantial performance increases across a variety of training regimes and model sizes. This work thus provides strong empirical evidence towards developing scaling laws for reinforcement learning.

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The Secret to Scaling Deep Reinforcement Learning: Mixtures of Experts

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