An introductory series to Reinforcement Learning (RL) with comprehensive step-by-step tutorials.

This is just a collection of projects that made during my DEEPLEARNING NANODEGREE by UDACITY

backtrader with DRL ( Deep Reinforcement Learning)

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

Some notebooks

Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow

Some awesome AI related books and pdfs for learning and downloading, also apply some playground models for learning

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

Implementation of Algorithms from the Policy Gradient Family. Currently includes: A2C, A3C, DDPG, TD3, SAC

Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). Examples of Logistic Regression, Linear Regression, Decision Trees, K-means clustering, Sentiment Analysis, Recommender Systems, Neural Networks and Reinforcement Learning.

Solutions of Reinforcement Learning, An Introduction

Reinforcement Learning Workshop for Data Science BKK

Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

Quiz & Assignment of Coursera

Notes for the Reinforcement Learning course by David Silver along with implementation of various algorithms.

Rainbow is all you need! A step-by-step tutorial from DQN to Rainbow

Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

Open Machine Learning course at MIPT

Attempt at reinforcement learning with curiosity for Sonic the Hedgehog games. Number 149 on OpenAI retro contest leaderboard, but more work needed

Implementation of Reinforcement Learning algorithms in Python, based on Sutton's & Barto's Book (Ed. 2)