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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LQL turns n-step action-sequence lower bounds into a practical hinge-loss stabilizer for off-policy Q-learning without extra networks or forward passes.
A tutorial playbook that organizes statistical evaluation into a workflow of claim, hypothesis, unit of analysis, baselines, sweeps, uncertainty, validation, and reporting, illustrated with Python code and a job-scheduling example.
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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Long-Horizon Q-Learning: Accurate Value Learning via n-Step Inequalities
LQL turns n-step action-sequence lower bounds into a practical hinge-loss stabilizer for off-policy Q-learning without extra networks or forward passes.
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How to Do Statistical Evaluations in ECE/CS Papers: A Practical Playbook for Defensible Results
A tutorial playbook that organizes statistical evaluation into a workflow of claim, hypothesis, unit of analysis, baselines, sweeps, uncertainty, validation, and reporting, illustrated with Python code and a job-scheduling example.