acktr | TensorFlow Implementation of Scalable trust | Reinforcement Learning library

by dyelax Python Version: Current License: MIT

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

kandi X-RAY | acktr Summary

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

An actor-critic model in TensorFlow, using KFAC loss, as descriibed in: "Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation" by Wu et al. Tested on Atari games.

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Support

acktr has a low active ecosystem.

It has 9 star(s) with 3 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

acktr has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of acktr is current.

Quality

acktr has 0 bugs and 0 code smells.

Security

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

acktr code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

acktr 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

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

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

It has 1090 lines of code, 106 functions and 11 files.

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

Top functions reviewed by kandi - BETA

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

A worker that runs the worker
Reset all the remotes
Plot results
Load monitor results
Plot a curve
Calculate a window of a rolling window
Compute a rolling window of an array
Convert a ts to x y coordinates

Get all kandi verified functions for this library.

acktr Key Features

No Key Features are available at this moment for acktr.

acktr Examples and Code Snippets

No Code Snippets are available at this moment for acktr.

Community Discussions

Trending Discussions on acktr

TypeError: __init__() missing 1 required positional argument: 'units' in LSTMCell

QUESTION

TypeError: __init__() missing 1 required positional argument: 'units' in LSTMCell

Asked 2020-Apr-02 at 19:13

I'm trying to implement Temporal attention in a Reinforcement Learning problem using Stable baselines however, I keep getting the mentioned error in the customer policy. I am using TensorFlow version 1.14. While using an LSTMCell along with RNN class from TensorFlow in my policy.py, I am also initializing a wrapper for attention but I keep getting the following error.

...

ANSWER

Answered 2020-Apr-02 at 19:13

According to the doc of LSTMCell, it requires a mandatory units parameters first, that is the dimensionality of the output space.

When you call its __init__() at the error line, you need to use __init__(units, ...).

Source https://stackoverflow.com/questions/60998288

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

Vulnerabilities

No vulnerabilities reported

Install acktr

You can download it from GitHub.
You can use acktr 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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