Off-policy RL algorithms can be sample-efficient for continuous control via sample multiple reuse

DOI10.1016/j.ins.2024.120371MaRDI QIDQ6539379

Xiu Li, Zongqing Lu, Le Wan, Jiafei Lyu

Publication date: 14 May 2024

Published in: Information Sciences (Search for Journal in Brave)

zbMATH Keywords

continuous controlreinforcement learningsample efficiency

Mathematics Subject Classification ID

Computer science (68-XX) Operations research, mathematical programming (90-XX)

Cites Work

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• Convergence results for single-step on-policy reinforcement-learning algorithms
• \({\mathcal Q}\)-learning
• Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction
• Multi-actor mechanism for actor-critic reinforcement learning

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