muzero | python implemenation of tabular MuZero | Predictive Analytics library

by wulfebw Python Version: Current License: MIT

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 Analytics, Predictive Analytics, Numpy applications. muzero has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

A python implemenation of tabular MuZero for educational purposes

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muzero has a low active ecosystem.

It has 19 star(s) with 1 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

muzero has no issues reported. There are no pull 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 is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

Build file is available. You can build the component from source.

Installation instructions are available. Examples and code snippets are not available.

muzero saves you 291 person hours of effort in developing the same functionality from scratch.

It has 703 lines of code, 77 functions and 24 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.

Runmuzero model
Returns a list of trajectories
Log env info
Log a summary of a step
Train the model
Perform a single step
Determine if the reward is terminal
Convert an action index to a tuple
Return the next state in the circuit
Returns a list of transitions for the given state and action
Perform a single action
Return a function to convert a space to index
Returns a function that converts a space to a space
Prints the figure
Return a list of trajectories
Returns the transition of a transition matrix
Predict the distribution of a given variable
Render a value 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

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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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