PolicyGradient | simple implementation of policy gradient method | Machine Learning library

by sergeyprokudin Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | PolicyGradient Summary

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

Notebook with simple implementation of policy gradient method (likelihood ratio estimation)

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PolicyGradient has a low active ecosystem.

It has 5 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of PolicyGradient is current.

Quality

PolicyGradient has no bugs reported.

Security

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

License

PolicyGradient 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.

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

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

Top functions reviewed by kandi - BETA

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

Checks whether P and R
Check that matrix is square stochastic
Checks that the given reward is valid
Checks that arrays of arrays have the same shape
Runs the policy iteration
Compute the policy transition matrix
Evaluate the policy matrix
Evaluate a policy
Generate random transition matrix
Generate random sparse matrix
Generate random matrix
Compute the reward for a given action
Compute the reward for an array
Compute a vector reward
Compute the bounding policy for a given value iteration
Evaluate the Bellman operator
Runs the Bellman operator
Runs the modified policy iteration
Run Bellman operator
Run the linear programming algorithm

Get all kandi verified functions for this library.

PolicyGradient Key Features

No Key Features are available at this moment for PolicyGradient.

PolicyGradient Examples and Code Snippets

No Code Snippets are available at this moment for PolicyGradient.

Community Discussions

Trending Discussions on PolicyGradient

How are input tensors with different shapes fed to neural network?

Unknown Length Array, Assigning Any Part Of The Array Any Time

Reinforcement Learning, how can I sample action from Gaussian distribution with action dimension space larger than one?

QUESTION

How are input tensors with different shapes fed to neural network?

Asked 2021-Feb-22 at 11:44

I am following this tutorial on Policy Gradient using Keras, and can't quite figure out the below.

In the below case, how exactly are input tensors with different shapes fed to the model?
Layers are neither .concated or .Added.

input1.shape = (4, 4)
input2.shape = (4,)
"input" layer has 4 neurons, and accepts input1 + input2 as 4d vector??

The code excerpt (modified to make it simpler) :

...

ANSWER

Answered 2021-Feb-22 at 11:44

In cases where you might want to figure out what type of graph you have just build, it is helpful to use the model.summary() or tf.keras.utils.plot_model() methods for debugging:

Source https://stackoverflow.com/questions/66311025

QUESTION

Unknown Length Array, Assigning Any Part Of The Array Any Time

Asked 2019-Dec-16 at 03:23

I am working with Q-Learning and want a 3D policy gradient that is completely empty until the the AI needs to access it.
This is because my state is three inputs that each could be any integer from 1 to infinity, each number above 1 being increasingly less probable.

Hopefully this is possible. I am also not looking for the code to be handed to me, just hope someone can point me in the right direction.

...

ANSWER

Answered 2019-Dec-16 at 03:14

You could use a dict-of-dict-of-dicts, but if you don't need to index on any particular state input, you could just use a dict with tuples of keys:

Source https://stackoverflow.com/questions/59350050

QUESTION

Reinforcement Learning, how can I sample action from Gaussian distribution with action dimension space larger than one?

Asked 2018-Sep-26 at 11:15

In the code of Actor-Critic with Gaussian,

...

ANSWER

Answered 2018-Sep-26 at 11:15

To create an action vector with shape (40), you need the last layer of your network to output a vector with a shape of 40. So change:

Source https://stackoverflow.com/questions/51260136

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

Vulnerabilities

No vulnerabilities reported

Install PolicyGradient

You can download it from GitHub.
You can use PolicyGradient 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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