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The Benefits of Being Distributional: Small-Loss Bounds for ...
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由 K Wang 著作2023被引用 16 次 — Abstract page for arXiv paper 2305.15703: The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning.
The Benefits of Being Distributional: Small-Loss Bounds for...
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由 K Wang 著作被引用 16 次 — TL;DR: We provide an explanation for the benefits of distributional RL through the lens of small-loss bounds, which scale with the instance-dependent optimal ...
The Benefits of Being Distributional: Small-Loss Bounds for ...
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由 K Wang 著作被引用 16 次 — In both online [Yang et al., 2019] and offline RL [Ma et al., 2021], distributional RL (DistRL) algorithms often perform better and use fewer samples in.
The benefits of being distributional - ACM Digital Library
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2024年5月30日 — This paper explains the benefits of DistRL through the lens of small-loss bounds, which are instance-dependent bounds that scale with optimal achievable cost.
Small-Loss Bounds for Reinforcement Learning
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The benefits of DistRL is explained through the lens of small-loss bounds, which are instance-dependent bounds that scale with optimal achievable cost and ...
Benefits of Being Distributional: Second-Order Bounds for ...
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由 K Wang 著作2024被引用 9 次 — In this paper, we prove that Distributional Reinforcement Learning (DistRL), which learns the return distribution, can obtain second-order bounds in both ...
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2024年7月22日 — While distributional reinforcement learning (DistRL) has been empirically effective, the question of when and why it is better than vanilla, non ...
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2023年5月26日 — While distributional reinforcement learning (DistRL) has been empirically effective, the question of when and why it is better than vanilla, non ...
More benefits of being distributional - ACM Digital Library
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由 K Wang 著作2024被引用 9 次 — In this paper, we prove that Distributional Reinforcement Learning (DistRL), which learns the return distribution, can obtain second-order ...
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We show that distributional RL enables faster learning when the systems have low variance. This holds for contextual bandits, online and offline RL ...