Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Reinforcement Learning | 4,090 | 4 years ago | 2 | mit | Jupyter Notebook | |||||
Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning | ||||||||||
Deep_reinforcement_learning_course | 3,581 | a year ago | 46 | Jupyter Notebook | ||||||
Implementations from the free course Deep Reinforcement Learning with Tensorflow and PyTorch | ||||||||||
Basic_reinforcement_learning | 978 | 8 months ago | 3 | gpl-3.0 | Jupyter Notebook | |||||
An introductory series to Reinforcement Learning (RL) with comprehensive step-by-step tutorials. | ||||||||||
Mushroom Rl | 759 | 4 | a month ago | 20 | October 30, 2023 | 5 | mit | Python | ||
Python library for Reinforcement Learning. | ||||||||||
Trading Bot | 758 | 4 months ago | 16 | mit | Jupyter Notebook | |||||
Stock Trading Bot using Deep Q-Learning | ||||||||||
Hands On Reinforcement Learning With Python | 596 | 3 years ago | 2 | Jupyter Notebook | ||||||
Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow | ||||||||||
Awesome Monte Carlo Tree Search Papers | 537 | 9 months ago | cc0-1.0 | Python | ||||||
A curated list of Monte Carlo tree search papers with implementations. | ||||||||||
Deer | 481 | 3 | 10 months ago | 12 | December 29, 2020 | 4 | other | Python | ||
DEEp Reinforcement learning framework | ||||||||||
Agentnet | 293 | 7 years ago | 5 | other | Python | |||||
Deep Reinforcement Learning library for humans | ||||||||||
Accel Brain Code | 289 | 2 | 3 months 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. |