AutoBench | Autonomous vehicle training environment | Machine Learning library

by karta1297963 Python Version: Current License: Apache-2.0

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

kandi X-RAY | AutoBench Summary

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

AutoBench is an open-source project base on Unity ML-Agents Toolkit featuring high configurability including difficulty, rewards, weather conditions, and visual observation types. Using REAL driving license exam in Taiwan as an example to showcase the applicability of autonomous vehicle in reinforcement learning approach with configurable difficulty technique.

Support

Quality

Security

License

Reuse

Support

AutoBench has a low active ecosystem.

It has 22 star(s) with 2 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of AutoBench is current.

Quality

AutoBench has 0 bugs and 0 code smells.

Security

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

AutoBench code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

AutoBench 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

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

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

Steps a single action
Copy a UnityInput object into a new UnityInput object
Flattens an array
Generate a UnityRLInput
Start learning process
Return True if the given lesson has already been filled
Increment the lesson
Increments the lesson for the given measure values
Run a training experiment
Start training
Extract camera configuration
Reset configuration
Adds experience to training buffer
Update the policy
Updates the feed dictionary
Determine the action for each agent
Iterate through the agents and process them
Create the grpc server
Exports the model
Get environment configuration
Launch executable launcher
Process all agents
Add agents to the training buffer
Create the DC actor critic
Create the encoders for the next visual observation
Create a ccc actor critic
Update the feed dictionary

Get all kandi verified functions for this library.

AutoBench Key Features

No Key Features are available at this moment for AutoBench.

AutoBench Examples and Code Snippets

No Code Snippets are available at this moment for AutoBench.

Community Discussions

Trending Discussions on AutoBench

Invalid Criterion report when testing on large inputs with AutoBench

QUESTION

Invalid Criterion report when testing on large inputs with AutoBench

Asked 2020-Aug-19 at 19:25

I am working with AutoBench since a few days testing performances of Euler's sieve on different input sizes.

My tests simply asks for the nth prime inside the list generated by Euler's sieve.

While Criterion works well on small inputs for n, it doesn't seem to produce a valid report when n is greater than 7000.

Here is my Input.hs file tested:

...

ANSWER

Answered 2020-Aug-19 at 19:25

After some profiling I found that for n greater than 7000, the Euler procedure quickly saturates the ram thus causing Criterion to crash.

The only ways to overcome this problem are increasing your ram or switching to a different algorithm/implementation.

Source https://stackoverflow.com/questions/63451144

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

Vulnerabilities

No vulnerabilities reported

Install AutoBench

You can download it from GitHub.
You can use AutoBench 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.