| andri27-ts/Reinforcement-Learning |
3,928 |
|
0 |
0 |
almost 6 years ago |
0 |
|
2 |
mit |
Jupyter Notebook |
| Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning |
| simoninithomas/Deep_reinforcement_learning_Course |
3,581 |
|
0 |
0 |
about 3 years ago |
0 |
|
46 |
|
Jupyter Notebook |
| Implementations from the free course Deep Reinforcement Learning with Tensorflow and PyTorch |
| vmayoral/basic_reinforcement_learning |
978 |
|
0 |
0 |
almost 3 years ago |
0 |
|
3 |
gpl-3.0 |
Jupyter Notebook |
| An introductory series to Reinforcement Learning (RL) with comprehensive step-by-step tutorials. |
| pskrunner14/trading-bot |
758 |
|
0 |
0 |
over 2 years ago |
0 |
|
16 |
mit |
Jupyter Notebook |
| Stock Trading Bot using Deep Q-Learning |
| MushroomRL/mushroom-rl |
746 |
|
0 |
4 |
over 2 years ago |
20 |
October 30, 2023 |
5 |
mit |
Python |
| Python library for Reinforcement Learning. |
| sudharsan13296/Hands-On-Reinforcement-Learning-With-Python |
596 |
|
0 |
0 |
over 5 years ago |
0 |
|
2 |
|
Jupyter Notebook |
| Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow |
| benedekrozemberczki/awesome-monte-carlo-tree-search-papers |
537 |
|
0 |
0 |
almost 3 years ago |
0 |
|
0 |
cc0-1.0 |
Python |
| A curated list of Monte Carlo tree search papers with implementations. |
| VinF/deer |
481 |
|
3 |
0 |
almost 3 years ago |
12 |
December 29, 2020 |
4 |
other |
Python |
| DEEp Reinforcement learning framework |
| yandexdataschool/AgentNet |
293 |
|
0 |
0 |
almost 9 years ago |
0 |
|
5 |
other |
Python |
| Deep Reinforcement Learning library for humans |
| accel-brain/accel-brain-code |
289 |
|
0 |
2 |
over 2 years ago |
12 |
July 26, 2022 |
1 |
gpl-2.0 |
Python |
| The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing. |
