RL-Storage applies deep Q-learning to storage parameter tuning and claims up to 2.6x throughput gains and 43% latency reduction, but the evidence is not rigorously presented.
Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System
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This paper explores multi-scenario optimization on large platforms using multi-agent reinforcement learning (MARL). We address this by treating scenarios like search, recommendation, and advertising as a cooperative, partially observable multi-agent decision problem. We introduce the Multi-Agent Recurrent Deterministic Policy Gradient (MARDPG) algorithm, which aligns different scenarios under a shared objective and allows for strategy communication to boost overall performance. Our results show marked improvements in metrics such as click-through rate (CTR), conversion rate, and total sales, confirming our method's efficacy in practical settings.
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Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques
RL-Storage applies deep Q-learning to storage parameter tuning and claims up to 2.6x throughput gains and 43% latency reduction, but the evidence is not rigorously presented.