Muzero-unplugged
pytorch-A3C
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| Muzero-unplugged | pytorch-A3C | |
|---|---|---|
| 3 | 3 | |
| 20 | 568 | |
| - | - | |
| 10.0 | 0.0 | |
| about 1 year ago | about 1 year ago | |
| Python | Python | |
| GNU General Public License v3.0 only | MIT License |
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Muzero-unplugged
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Show HN: Ghidra Plays Mario
https://github.com/DHDev0/Muzero-unplugged
Gym is now gymnasium and it has support for additional Environments like Mujoco:
- Implementation of MuZero, MuZero Unplugged and Stochastic MuZero
pytorch-A3C
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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.
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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
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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?
Stochastic-muzero - Pytorch Implementation of Stochastic MuZero for gym environment. This algorithm is capable of supporting a wide range of action and observation spaces, including both discrete and continuous variations.
salina - a Lightweight library for sequential learning agents, including reinforcement learning
Muzero - Pytorch Implementation of MuZero for gym environment. It support any Discrete , Box and Box2D configuration for the action space and observation space.
tianshou - An elegant PyTorch deep reinforcement learning library.
nn-morse - Decode morse using a neural network
pytorch-a3c - PyTorch implementation of Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning".
ghidra-tlcs900h - Ghidra processor module for Toshiba TLCS-900/H
Note - Easily implement parallel training and distributed training. Machine learning library. Note.neuralnetwork.tf package include Llama2, Llama3, CLIP, ViT, ConvNeXt, SwiftFormer, etc, these models built with Note are compatible with TensorFlow and can be trained with TensorFlow.
retro - Retro Games in Gym
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
neural-network-scratch - build a neural network to show as a demonstration on inner workings of a neural network
muzero-general - MuZero