I want to build a multi-agent RL environment from scratch and then want to use Deep Q learning to solve the environment. Can anyone suggest what should be the pipeline?
I'm familiar with the OpenAi gym custom environment.
Related
Anybody recommend any backends or frameworks for Forge?
I'm seeing resources for Nodejs, PHP, .Net Core and others which are for the backend.
Are any of these any more convenient or dependable with Forge than the others?
I also know Python and thought Django would be another option but I don't see too many resources on the Python side of things.
Any perspectives on the tools (pro or con) would be great.
The more I understand the kinds of tech stacks, user projects and ways people use Forge to expand on BIM 360 and other APIs the more it can help me and the community get familiar with the service.
This relies completely on the excisting stack used by your company. Forge is a collection of API's accessible via endpoints.
Any library just abstracts the calls away in a accesible way. I've had moderate succes with the dotnet core Forge package, it works very well but you are giving away some strict typing.
If you dont wanna be bound by abstractions made by other people, create your own ! This will ultimately lead to the most lightweight solution since you are only creating what you need.
Cheers
I want to create a gaming environment which can move a robot in the path specified
Is there a way to use any simulator to create such environment that can be integrated with gym ?
I have the client side program of a MMORPG.
The game sever have been stop running for about 7 years.
The game engine is proprietary so there should be no tools for resource extraction.
I have some basic programming skills. But not very knowledgeable about game programming. No experience on reverse engineering.
Any tips?
I have deployed a regression model on azure ML , is it possible to get the model weights/coefficients of the model programatically from azure, rather than getting predicted value? .
I think you can do so, in your training experiment add an output to your evaluate model module then select deploy webservice right away without going through the predictive experiment option.
Once You publish and click the TEST button You should the values as below
No. Currently we do not feature exporting weights from the models including with Azure Machine Learning.
If you have a method for extracting weights from Python models, you may be able to work this out using the execute Python Script module.
The primary purpose of Azure Machine Learning is to make deployable and scalable web services from the machine learning modules. Though the authoring experience for creating ML models is great, it is not intended to be a place to create and export models, but instead a place to create and operationalize your models.
UPDATE New features may make this answer outdated.
I am working in an enterprise with some legacy applications, which are quite coupled.
Integration test is thus very important. I am looking for a distributed testing framework, or a combination of them that QA team and development team can both leverage on. For "distributed" I refer to the many environments for testing, while a single application itself is not a distributed system.
The following criteria might not be the ideal model, but kind of requirement and expectation of the tool to be developed.
Such framework should be :
open sourced
community support to learn, maintain and extend :)
distributed
allow tests to be execute in multiple environments.
since test cases may change quite often, it should be managable to distributed those test into target machines with target codebase to run.
I am checking out [STAF][1] on this.
allow integration tests
I am looking into Camel testing. we have some existing "simulator", "mock" to stimulate some applications behavior.
framework with easy-to-use dependency injection / mock will be very helpful..
with WEB UI, wich is a single place that can
- all testing results are shown
- able to trigger test
- able to see testing definition
Human readable and configurable acceptance testing definition
Testing language should be in a readable DSL
I wonder is framework like FitNesse a good choice as I did not figure out a way to test it distributed.
For Citrus I am concerning XML is hard to read
Some current frameworks
My team has been suggesting JSystem, while it is possible to distribute tests, there is no web GUI and
my major concern is its development has been stopped since 2009
I looked into many frameworks as mentioned,
Grinder - seems better suite web load testing?
JMeter- this seems to be closer, but also focused on load test?
more on background: most developers are familiar with JAVA and currently we use Hudson to run JUnit tests.
And finally I am also thinking a more conceptual issue, should xUnit test, integration test, acceptance test all being separated? xUnit test should be stick with code base? perhaps this worth another question, but with that in mind I am not sure I am looking for the right thing.
I will greatly appreciate if you can give me some comments on the thought / suggest some frameworks. Thanks a lot in advance.
I will suggest you to use Jenkins Framework.
Open source
Can be used for different environments
I myself working over Integration Testing using Jenkins so suggest you for this.
Web UI is very interactive.
Large variety of plugins is available and development is still on.
Write some ANT, MAVEN scripts you can get your job done through this.