Practical_RL
PX4-user_guide
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| Practical_RL | PX4-user_guide | |
|---|---|---|
| 2 | 1 | |
| 5,716 | 268 | |
| 1.2% | 6.3% | |
| 6.0 | 9.9 | |
| 17 days ago | 3 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| The Unlicense | GNU General Public License v3.0 or later |
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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.
PX4-user_guide
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Pixhawk Power module for drone battery.
I have little to no idea what I am doing or how to start even when asking the owner(my lack of knowledge and experience). I have found what the pins are for and their required voltage but I don't know what else I can do. I even found some code through github (https://github.com/PX4/PX4-user_guide/tree/master/ja/power_module) idk what other information or whether if anything I find is important. I do plan on looking into the flight controller as well.
What are some alternatives?
webdataset - A high-performance Python-based I/O system for large (and small) deep learning problems, with strong support for PyTorch.
jellyfish - 🪼 a python library for doing approximate and phonetic matching of strings.
FunMatch-Distillation - TF2 implementation of knowledge distillation using the "function matching" hypothesis from https://arxiv.org/abs/2106.05237.
awesome-rl - Reinforcement learning resources curated
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
alpha-zero-general - A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
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
AIrsenal - Machine learning Fantasy Premier League team
awesome-RLHF - A curated list of reinforcement learning with human feedback resources (continually updated)