vojtamolda/reinforcement-learning-an-introduction
quality grade F, 26 out of 100Solutions to exercises in Reinforcement Learning: An Introduction (2nd Edition).
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RL algorithms, environments, simulators and decision-making systems.
Signals: reinforcement-learning, deep-reinforcement-learning, rl, gymnasium, openai-gym, multi-agent-reinforcement-learning, imitation-learning
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Solutions to exercises in Reinforcement Learning: An Introduction (2nd Edition).
Deep Reinforcement Learning Hands-On, 3E_Published by Packt
这是一个学习强化学习基础原理的仓库,主要包括了《深入浅出强化学习原理入门》书中一些例子和课后作业的代码
Reinforcement learning baseline agent trained with the Actor-critic (A3C) algorithm.
Learning Resources And Links Of Reinforcement Learning (updating)
My Exploration on Deep Reinforcement Learning Survey
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Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math
Reinforcement Learning and Decision Making tutorials explained at an intuitive level and with Jupyter Notebooks
kinds of reinforcement learning model by Pytorch
This code is written for the blogs
[Coursera] Reinforcement Learning Specialization by "University of Alberta" & "Alberta Machine Intelligence Institute"
書籍「つくりながら学ぶ!深層強化学習」のサポートリポジトリです
Julia code for the book Reinforcement Learning An Introduction
Using reinforcement learning to teach a car to avoid obstacles.
Reinforcement learning tutorials
Reinforcement Learning examples implementation and explanation
[파이썬과 케라스로 배우는 강화학습] 예제
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Every day, millions of traders around the world are trying to make money by trading stocks. These days, physical traders are also being replaced by automated trading robots. Algorithmic trading market has experienced significant growth rate and large number of firms are using it. I have tried to build a Deep Q-learning reinforcement agent model to do automated stock trading.
Implementation of the supervised learning experiments in Vector-based navigation using grid-like representations in artificial agents, as published at https://www.nature.com/articles/s41586-018-0102-6
A set of high-dimensional continuous control environments for use with Unity ML-Agents Toolkit.
This is a simple implementation of DeepMind's PySC2 RL agents.
Standardized environment infrastructure for Agentic AI development.
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