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强化学习中文教程（蘑菇书🍄），在线阅读地址：https://datawhalechina.github.io/easy-rl/
Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....
深度学习入门课、资深课、特色课、学术案例、产业实践案例、深度学习知识百科及面试题库The course, case and knowledge of Deep Learning and AI
Repository for most of the code from my YouTube channel
Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow
Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..
Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog
Implementing Reinforcement Learning, namely Q-learning and Sarsa algorithms, for global path planning of mobile robot in unknown environment with obstacles. Comparison analysis of Q-learning and Sarsa
A blog which talks about machine learning, deep learning algorithms and the Math. and Machine learning algorithms written from scratch.
Tutorial4RL: Tutorial for Reinforcement Learning. 强化学习入门教程.
Package provides java implementation of reinforcement learning algorithms such Q-Learn, R-Learn, SARSA, Actor-Critic
Implementations of Deep Reinforcement Learning Algorithms and Bench-marking with PyTorch
Paddle-RLBooks is a reinforcement learning code study guide based on pure PaddlePaddle.
Code repository for my course on the fundamentals of reinforcement learning
Basic reinforcement learning algorithms. Including:DQN,Double DQN, Dueling DQN, SARSA, REINFORCE, baseline-REINFORCE, Actor-Critic,DDPG,DDPG for discrete action space, A2C, A3C, TD3, SAC, TRPO
Reinforcement Learning Demos
Combining deep learning and reinforcement learning.
This Repository contains a series of google colab notebooks which I created to help people dive into deep reinforcement learning.This notebooks contain both theory and implementation of different algorithms.
Kotlin implementation of algorithms, examples, and exercises from the Sutton and Barto: Reinforcement Learning (2nd Edition)