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【第60期】RLTools：基于C++的开源强化学习工具
Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous ControlSummary
RLtools, a new open-source C++ library, significantly accelerates deep reinforcement learning (RL) for continuous control problems. Its header-only, dependency-free design enables fast training and inference across diverse platforms, from high-performance computers to microcontrollers. This speed improvement is demonstrated through benchmarks showing substantial performance gains over existing RL frameworks. A key contribution is the first-ever demonstration of training a deep RL algorithm directly on a microcontroller, opening the field of "TinyRL." The library's architecture, based on C++ templating and a novel static multiple-dispatch paradigm, is central to its speed and portability.
原文链接：https://arxiv.org/abs/2306.03530
庆祝完成两个月的更新～
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By 任雨山Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous ControlSummary
RLtools, a new open-source C++ library, significantly accelerates deep reinforcement learning (RL) for continuous control problems. Its header-only, dependency-free design enables fast training and inference across diverse platforms, from high-performance computers to microcontrollers. This speed improvement is demonstrated through benchmarks showing substantial performance gains over existing RL frameworks. A key contribution is the first-ever demonstration of training a deep RL algorithm directly on a microcontroller, opening the field of "TinyRL." The library's architecture, based on C++ templating and a novel static multiple-dispatch paradigm, is central to its speed and portability.
原文链接：https://arxiv.org/abs/2306.03530
庆祝完成两个月的更新～