DeepBeerInventory-RL
Note
DeepBeerInventory-RL | Note | |
---|---|---|
1 | 48 | |
72 | 35 | |
- | - | |
0.0 | 9.9 | |
about 1 year ago | 2 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
DeepBeerInventory-RL
Note
- Easily implement parallel training.
- This project allows you to easily implement parallel training with the multiprocessing module.
-
Train neural networks in parallel using Python's multiprocessing module.
https://github.com/NoteDancing/Note This project allows you to train neural network in parallel using Python's multiprocessing module.
- A system for deep learning and reinforcement learning.
- A system for deep learning and reinforcement learning. (r/MachineLearning)
- [P] A system for deep learning and reinforcement learning.
What are some alternatives?
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deep-RL-trading - playing idealized trading games with deep reinforcement learning
or-gym - Environments for OR and RL Research
deep-significance - Enabling easy statistical significance testing for deep neural networks.
Muzero-unplugged - Pytorch Implementation of MuZero Unplugged for gym environment. This algorithm is capable of supporting a wide range of action and observation spaces, including both discrete and continuous variations.
softlearning - Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.
Muzero - Pytorch Implementation of MuZero for gym environment. It support any Discrete , Box and Box2D configuration for the action space and observation space.
quickai - QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
abce - Agent-based computational Economics, the Python library that makes AB modelling easier
muzero-general - MuZero
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)