MuZero | A structured implementation of MuZero | Reinforcement Learning library

by johan-gras Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | MuZero Summary

MuZero is a Python library typically used in Artificial Intelligence, Reinforcement Learning, Deep Learning, Tensorflow applications. MuZero has no bugs, it has no vulnerabilities and it has low support. However MuZero build file is not available. You can download it from GitHub.

A structured implementation of MuZero

Support

Quality

Security

License

Reuse

Support

MuZero has a low active ecosystem.

It has 192 star(s) with 54 fork(s). There are 10 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. On average issues are closed in 232 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of MuZero is current.

Quality

MuZero has 0 bugs and 0 code smells.

Security

MuZero has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

MuZero code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

MuZero does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

MuZero releases are not available. You will need to build from source code and install.

MuZero has no build file. You will be need to create the build yourself to build the component from source.

MuZero saves you 284 person hours of effort in developing the same functionality from scratch.

It has 687 lines of code, 92 functions and 18 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed MuZero and discovered the below as its top functions. This is intended to give you an instant insight into MuZero implemented functionality, and help decide if they suit your requirements.

Train MuZeroNet
Apply an action to the step
Run evaluation
Play a game
Compute the value of the value
Softmax function
Return a configuration for cartpole
Inverse inference function

Get all kandi verified functions for this library.

MuZero Key Features

No Key Features are available at this moment for MuZero.

MuZero Examples and Code Snippets

No Code Snippets are available at this moment for MuZero.

Community Discussions

Trending Discussions on MuZero

Is the reward value in MuZero's pseudocode misaligned?

How to scale a gradient norm in Keras

QUESTION

Is the reward value in MuZero's pseudocode misaligned?

Asked 2020-Feb-21 at 18:09

MuZero, a deep reinforcement learning technique, was just released, and I've been trying to implement it by looking at its pseudocode and this helpful tutorial on Medium.

However, there's something confusing me about how rewards are handled during training in the pseudocode, and it would be great if someone could verify that I'm reading the code correctly, and if I am, explain why this training algorithm works.

Here's the training function (from the pseudocode):

...

ANSWER

Answered 2020-Feb-21 at 18:09

Author here.

What does the reward from the initial_inference represent?

The initial inference "predicts" the last observed reward. This isn't actually used for anything, but makes our code simpler: The prediction head can simply always predict the immediately preceding reward. For the dynamics network, this would be the reward observed after applying the action that's given as an input to the dynamics network.

At the beginning of the game there is no last observed reward, so we just set it to 0.

The reward target computation in the pseudocode was indeed misaligned; I've just uploaded a new version to arXiv.

Where it used to say

Source https://stackoverflow.com/questions/60234530

QUESTION

How to scale a gradient norm in Keras

Asked 2020-Jan-06 at 17:27

In the pseudocode for MuZero, they do the following:

...

ANSWER

Answered 2020-Jan-06 at 17:27

You can use the MaxNorm constraint presented here.

It's very simple and straightforward. Import it from keras.constraints import MaxNorm

If you want to apply it to weights, when you define a Keras layer, you use kernel_constraint = MaxNorm(max_value=2, axis=0) (read the page for details on axis)

You can also use bias_constraint = ...

If you want to apply it to any other tensor, you can simply call it with a tensor:

Source https://stackoverflow.com/questions/59616311

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install MuZero

You can download it from GitHub.
You can use MuZero like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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