hands-on-rl
Practical_RL
| hands-on-rl | Practical_RL | |
|---|---|---|
| 1 | 2 | |
| 984 | 5,755 | |
| - | 1.1% | |
| 2.8 | 6.0 | |
| 2 months ago | about 1 month ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | The Unlicense |
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hands-on-rl
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Intro RL Lectures
👉🏽 Lectures: http://datamachines.xyz/the-hands-on-reinforcement-learning-course/ 👉🏽 Github repo of the course: https://github.com/Paulescu/hands-on-rl
Practical_RL
- [D] implementation of MCTS in Python
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Alternatives to OpenAI’s spinning up?
there is this great github repo where there are lectures and other resources, and have a week by week jupyter notebooks where they explain and code with homeworks at the very end of it. is basics and deepRL, but just dqn and DDPG/ppo but i think will give you good start in the topic for later star working on your own.
What are some alternatives?
FinRL - FinRL: Financial Reinforcement Learning. 🔥
webdataset - A high-performance Python-based I/O system for large (and small) deep learning problems, with strong support for PyTorch.
FinRL-Library - Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance. NeurIPS 2020 & ICAIF 2021. 🔥 [Moved to: https://github.com/AI4Finance-Foundation/FinRL]
FunMatch-Distillation - TF2 implementation of knowledge distillation using the "function matching" hypothesis from https://arxiv.org/abs/2106.05237.
FinRL - Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance. NeurIPS 2020 & ICAIF 2021. 🔥 [Moved to: https://github.com/AI4Finance-Foundation/FinRL]
awesome-rl - Reinforcement learning resources curated
alpha-zero-general - A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
redisai-examples - RedisAI showcase
TensorFlow-Tutorials - TensorFlow Tutorials with YouTube Videos
YPDL-Build-a-movie-recommendation-engine-with-TensorFlow - In this tutorial, we are going to build a Restricted Boltzmann Machine using TensorFlow that will give us recommendations based on movies that have been watched already. The datasets we are going to use are acquired from GroupLens and contains movies, users, and movie ratings by these users.
rl-trading - Using Reinforcement Learning agents as Algorithmic Traders