d4rl_evaluations | repository contains the algorithms

by rail-berkeley Python Version: Current License: Apache-2.0

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

kandi X-RAY | d4rl_evaluations Summary

d4rl_evaluations is a Python library. d4rl_evaluations has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However d4rl_evaluations build file is not available. You can download it from GitHub.

This repository contains the algorithms used to evaluate tasks in the D4RL benchmark. All code is lightly modified from other public repositories on Github.

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Quality

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Support

d4rl_evaluations has a low active ecosystem.

It has 84 star(s) with 16 fork(s). There are 13 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 7 have been closed. On average issues are closed in 4 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of d4rl_evaluations is current.

Quality

d4rl_evaluations has 0 bugs and 0 code smells.

Security

d4rl_evaluations has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

d4rl_evaluations code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

d4rl_evaluations is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

d4rl_evaluations releases are not available. You will need to build from source code and install.

d4rl_evaluations has no build file. You will be need to create the build yourself to build the component from source.

d4rl_evaluations saves you 9493 person hours of effort in developing the same functionality from scratch.

It has 19379 lines of code, 1372 functions and 179 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed d4rl_evaluations and discovered the below as its top functions. This is intended to give you an instant insight into d4rl_evaluations implemented functionality, and help decide if they suit your requirements.

Run an experiment .
Format table data as a table .
Train and eval .
Train and evaluate the model .
Solves a skewfit experiment .
Train the model using TensorFlow .
Gets VAE environment variables .
Refreshes the decoded images .
Configure the logger .
Generate a VAE dataset from parameters .

Get all kandi verified functions for this library.

d4rl_evaluations Key Features

No Key Features are available at this moment for d4rl_evaluations.

d4rl_evaluations Examples and Code Snippets

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Community Discussions

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Vulnerabilities

No vulnerabilities reported

Install d4rl_evaluations

You can download it from GitHub.
You can use d4rl_evaluations 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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