2023年5月30日 · Specifically, we develop reinforcement learning (RL) based models to decide how to choose a subtree for insertion and how to split a node when building and updating an R-Tree, instead of relying on hand-crafted heuristic rules currently used by …

李宏毅老师的《深度强化学习》是强化学习领域经典的中文视频之一。 李老师幽默风趣的上课风格让晦涩难懂的强化学习理论变得轻松易懂，他会通过很多有趣的例子来讲解强化学习理论。 比如老师经常会用玩 Atari 游戏的例子来讲解强化学习算法。 此外，为了教程的完整性，我们整理了周博磊老师的《强化学习纲要》、李科浇老师的《世界冠军带你从零实践强化学习》以及多个强化学习的经典资料作为补充。 对于想入门强化学习又想看中文讲解的人来说绝对是非常推荐的。 本 …

2021年3月8日 · Specifically, we develop reinforcement learning (RL) based models to decide how to choose a subtree for insertion and how to split a node when building an R-Tree, instead of relying on hand-crafted heuristic rules currently used by R-Tree and its variants.

2020年1月25日 · 前面写过RL中t+1是在agent做出action后发生的，仍旧是当前状态下，即意思是不管在这个状态下做什么action，Rs=Rt+1都一定的。 γ是一个未来对现在影响的数学上的表达，γ=0，完全短视不考虑未来，γ=1，undiscount未来的所有状态都考虑。

2024年2月2日 · In this work, we review research studies that combine Reinforcement Learning (RL) and Large Language Models (LLMs), two areas that owe their momentum to the development of deep neural networks. We propose a novel taxonomy of three main classes based on the way that the two model types interact with each other.

本项目用于学习rl基础算法，主要面向对象为rl初学者、需要结合rl的非专业学习者，尽量做到: 注释详细，结构清晰。 注意本项目为实战内容，建议首先掌握相关算法的一些理论基础，再来享用本项目，理论教程参考本人参与编写的 蘑菇书 。

2021年3月8日 · Specifically, we develop reinforcement learning (RL) based models to decide how to choose a subtree for insertion and how to split a node, instead of relying on hand-crafted heuristic rules as R-Tree and its variants. Experiments on real and synthetic datasets with up to 100 million spatial objects clearly show that our RL based index ...

2024年2月1日 · We propose a novel evolutionary algorithm for interpretable RL, based on a multi-method ensemble optimization algorithm. We develop a method capable of pruning DTs in RL environments, inspired by traditional DT pruning methods from supervised learning.

Specifically, we develop reinforcement learning (RL) based models to decide how to choose a subtree for insertion and how to split a node, instead of relying on hand-crafted heuristic rules as R-Tree

Specifically, we develop reinforcement learning (RL) based models to decide how to choose a subtree for insertion and how to split a node when building and updating an R-Tree, instead of relying on hand-crafted heuristic rules currently used by the R-Tree and its variants.