PE-MAMoE combines sparsely gated mixture-of-experts actors with a non-parametric phase controller in MAPPO to maintain plasticity under dynamic user mobility and traffic, yielding 26.3% higher normalized IQM return in simulations.
Mixture of experts in a mixture of reinforcement learning settings
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Mixtures of Experts (MoEs) have gained prominence in (self-)supervised learning due to their enhanced inference efficiency, adaptability to distributed training, and modularity. Previous research has illustrated that MoEs can significantly boost Deep Reinforcement Learning (DRL) performance by expanding the network's parameter count while reducing dormant neurons, thereby enhancing the model's learning capacity and ability to deal with non-stationarity. In this work, we shed more light on MoEs' ability to deal with non-stationarity and investigate MoEs in DRL settings with "amplified" non-stationarity via multi-task training, providing further evidence that MoEs improve learning capacity. In contrast to previous work, our multi-task results allow us to better understand the underlying causes for the beneficial effect of MoE in DRL training, the impact of the various MoE components, and insights into how best to incorporate them in actor-critic-based DRL networks. Finally, we also confirm results from previous work.
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A device–server split of recurrent latent LLM reasoning plus semantic MoE-SAC scheduling yields about 18% higher simulated system throughput than plain SAC under energy, recurrence, and latency budgets.
MuSix introduces scale-aware world model mixtures with experiential-distance routing and adaptive forgetting to improve multi-scale reasoning and dynamic adaptation in embodied agents.
citing papers explorer
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Plasticity-Enhanced Multi-Agent Mixture of Experts for Dynamic Objective Adaptation in UAVs-Assisted Emergency Communication Networks
PE-MAMoE combines sparsely gated mixture-of-experts actors with a non-parametric phase controller in MAPPO to maintain plasticity under dynamic user mobility and traffic, yielding 26.3% higher normalized IQM return in simulations.
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MORES: Mobile Reasoning-as-a-Service via Distributed LLM Inference-Time Scaling
A device–server split of recurrent latent LLM reasoning plus semantic MoE-SAC scheduling yields about 18% higher simulated system throughput than plain SAC under energy, recurrence, and latency budgets.
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Multi-scale Mixture of World Models for Embodied Agents in Evolving Environments
MuSix introduces scale-aware world model mixtures with experiential-distance routing and adaptive forgetting to improve multi-scale reasoning and dynamic adaptation in embodied agents.