Title: Global Neural Inhibition Improves Mixture of Experts in Multi-task Reinforcement Learning
Primary area: reinforcement learning
Keywords: neural inhibition, deep learning, gating, mixture of experts, dynamic routing

Abstract: Dynamic neural architectures enhance computational efficiency and model expressivity by selectively routing inputs through specialized pathways. Routing mechanisms are instrumental to mixture-of-experts (MoE) models, and their design remains challenging. To enhance and expand the routing architecture, we propose  Global Recurrent Inhibition Networks (GRIN) to apply inhibitory gating to mixture weights, dynamic routers and output representations. The approach employs a recurrent algorithm that integrates signals from all neurons to maximize connection diversity. Inspired by biological inhibitory neurons, GRIN is designed to dynamically modulate the neural network, suppress redundant signals, and improve pathway selectivity. We experiment with a cascaded architecture that progressively diversifies connections to the inhibitory gate, demonstrating that connection diversity correlates with performance improvements. Empirical evaluation across simulated vision, language modeling, and multi-task reinforcement learning (MTRL) demonstrates consistent improvements over baseline MoE architectures, with improved performance on the challenging MetaWorld MT10 and MT50 benchmarks.

TL;DR: We propose global neural inhibition network and empirically shows improvements across mixture-of-experts architectures, particularly in multi-task reinforcement learning.