FinRL®: Financial Reinforcement Learning. 🔥
Our codebase trials provide an implementation of the Select and Trade paper, which proposes a new paradigm for pair trading using hierarchical reinforcement learning. It includes the code for the proposed method and experimental results on real-world stock data to demonstrate its effectiveness.
Automated stock trading strategy using deep reinforcement learning and recurrent neural networks
A Complete Collection of Deep RL Famous Algorithms implemented in Gymnasium most Popular environments
本项目是作者（MRL Liu）使用AI算法的强化学习方法玩迷宫游戏的一个阶段性总结，本项目的迷宫游戏是简单的方格迷宫，其状态空间和动作空间都足够简单，是作者整理的手中的第1个RL项目。该项目重构了作者之前学习时的一些基于Value的RL算法，将它们的例如经验回放池的对象等抽象出来为一个对象，便于整理知识网络。该项目的原始算法代码使用的是莫烦Python的相关实现，在此向莫烦老师表示感谢。本项目的特色是使用了统一范式的代码来定义基于Value的算法系列的实现，封装了Q-Table和ReplayBuffer对象；添加了网络模型的保存与加载功能、TensorFlow可视化功能、经验池保存和加载等。整个项目基于良好的面向对象思想，方法定义层层推进。
Financial trading strategies using deep reinforcement learning (DRL). It offers a frameworks for quantitative finance, enabling practitioners to create, test, and implement investments strategies.
Performance evaluation of several DRL algorithms in a discrete action-space for resource allocation in Open RAN
A modular, high-level Python package for Deep Reinforcement Learning, designed to simplify the implementation and study of DRL algorithms, offering an accessible and extensible framework for students, researchers, and developers.
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