April 2, 2024, 7:42 p.m. | Yibo Wang, Jiang Zhao

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.00651v1 Announce Type: new
Abstract: Recent advancements in deep reinforcement learning (RL) have demonstrated notable progress in sample efficiency, spanning both model-based and model-free paradigms. Despite the identification and mitigation of specific bottlenecks in prior works, the agent's exploration ability remains under-emphasized in the realm of sample-efficient RL. This paper investigates how to achieve sample-efficient exploration in continuous control tasks. We introduce an RL algorithm that incorporates a predictive model and off-policy learning elements, where an online planner enhanced by …

abstract agent arxiv bottlenecks cs.ai cs.lg efficiency exploration free identification intrinsic motivation paper policy prior progress realm reinforcement reinforcement learning sample type

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