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Chester is a tool aiming at automatically launching experiments. This tool based on rllab(https://github.com/rll/rllab ), and further extended for launching and retrieving experiments in different remote machines, including:
Seuss
PSC (Pittsburgh Super Computing)
Getting Started
We've provided an example for launching experiments of openai/baseline's DDPG algorithm.
Look into the /examples, you'll find 'train_luanch.py' and 'train.py'. 'train.py' is the parser where we copied a lot of codes in openai/baseline/ddpg/main.py and combined them as a function 'run_task'. 'run_task' receives the parameters and start running the DDPG algorithm with those given settings.
The launcher 'train_launch.py' uses our chester and the 'run_task' function to launch a group of experiments locally. By running this launcher, the group experiments are started and the resutls are contained in one given folder. Those result files are able to be visulized with rllab's viskit.
To support different options in visulization, chester provided self-written interface 'preset.py'. The author can write different custom splitters in this file and put it in the directory for experiments. The viskit tool can detect this preset file and apply different options.
Prerequisites
What things you need to install the software and how to install them
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Installation
A step by step series of examples that tell you how to get a development env running
Say what the step will be
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And repeat
until finished
End with an example of getting some data out of the system or using it for a little demo
Running the tests
Explain how to run the automated tests for this system
Break down into end to end tests
Explain what these tests test and why
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And coding style tests
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Deployment
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