Note
pytorch-A3C
| Note | pytorch-A3C | |
|---|---|---|
| 48 | 3 | |
| 35 | 568 | |
| - | - | |
| 9.9 | 0.0 | |
| 4 days ago | about 1 year ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
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.
pytorch-A3C
-
Formula to compute loss in A3C
I'm a beginner to RL and I'm trying to understand how the loss function was computed. If it follows a specific formular. I've read the a3c algorithm overview on paper by barto but it seems the implemtation here https://github.com/MorvanZhou/pytorch-A3C/blob/master/discrete_A3C.py is different.
-
How to measure the performance of a3c algorithm
I'm new to RL and i just started going through this implementation of a3c https://github.com/MorvanZhou/pytorch-A3C
-
Tensorflow vs PyTorch for A3C
For the A3C part, I would appreciate your insights on whether to use Tensorflow or PyTorch to implement the algorithm. This GitHub https://github.com/MorvanZhou/pytorch-A3C tries to explain some things but it still isn't very clear to me which is the best, as I see that many implementations with TensorFlow. So if you have anything to add to help me choose one framework, I would very thankful.
What are some alternatives?
deep-RL-trading - playing idealized trading games with deep reinforcement learning
salina - a Lightweight library for sequential learning agents, including reinforcement learning
deep-significance - Enabling easy statistical significance testing for deep neural networks.
tianshou - An elegant PyTorch deep reinforcement learning library.
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-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.
quickai - QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
pytorch-a3c - PyTorch implementation of Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning".
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)