DeepRL | Tested with Ubuntu | Machine Learning library

by gabrieledcjr Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | DeepRL Summary

DeepRL is a Python library typically used in Artificial Intelligence, Machine Learning, Tensorflow applications. DeepRL has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Tested with Ubuntu 16.04 and Tensorflow r1.11.

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Quality

Security

License

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Support

DeepRL has a low active ecosystem.

It has 14 star(s) with 7 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 2 have been closed. On average issues are closed in 1 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of DeepRL is current.

Quality

DeepRL has 0 bugs and 0 code smells.

Security

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

DeepRL code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

DeepRL 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

DeepRL 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 not available. Examples and code snippets are available.

DeepRL saves you 6534 person hours of effort in developing the same functionality from scratch.

It has 13578 lines of code, 812 functions and 95 files.

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

Top functions reviewed by kandi - BETA

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

Run tensorflow
Calculate anneal learning rate
Set weights to weight
Train the model
Create ae classification example
Test the policy
Choose the action with the highest confidence
Reconstruct the image
Multi game
Get a demo
Create a classification example
Test the game
A gradient descent optimizer
Sample the response
Sample from the distribution
Plot test results
Return a list of the encoder vars
Update the robot action
Prepare the loss
Plot convolutional output
Extracts the models
Plot convolutional weights
Run dqn
Get demo
Prepare loss
View features
Load the network from a pretrain model
Test the test environment

Get all kandi verified functions for this library.

DeepRL Key Features

No Key Features are available at this moment for DeepRL.

DeepRL Examples and Code Snippets

No Code Snippets are available at this moment for DeepRL.

Community Discussions

Trending Discussions on DeepRL

DRQN - Prefix tensor must be either a scalar or vector, but saw tensor

QUESTION

DRQN - Prefix tensor must be either a scalar or vector, but saw tensor

Asked 2017-Jul-25 at 02:51

In following this tutorial, I am receiving the following error:

ValueError: prefix tensor must be either a scalar or vector, but saw tensor: Tensor("Placeholder_2:0", dtype=int32)

The error originates from these lines:

...

ANSWER

Answered 2017-Jul-24 at 01:52

I met the same problem with the version of tensorflow is 1.2.+.

When i changed it to 1.1.0, the problem resolved.

I think it because the API of rnn_cell.zero_state makes arg batch_size must be a scalar or vector, but not tensor.

So, if you change batch_size to scalar, e.g. 128, the problem also could be resolved.

Source https://stackoverflow.com/questions/44961181

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

Vulnerabilities

No vulnerabilities reported

Install DeepRL

You can download it from GitHub.
You can use DeepRL 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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