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b463d9e5c7
* Initializing A3C code * Modifications for Ray usage * cleanup * removing universe dependency * fixes (not yet working * hack * documentation * Cleanup * Preliminary Portion Make sure to change when merging * RL part * Cleaning up Driver and Worker code * Updating driver code * instructions... * fixed * Minor changes. * Fixing cmake issues * ray instruction * updating port to new universe * Fix for env.configure * redundant commands * Revert scipy.misc -> cv2 and raise exception for wrong gym version. |
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| doc | ||
| docker | ||
| examples | ||
| python | ||
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| src | ||
| test | ||
| vsprojects | ||
| webui | ||
| .clang-format | ||
| .editorconfig | ||
| .gitignore | ||
| .travis.yml | ||
| build-docker.sh | ||
| build.sh | ||
| CMakeLists.txt | ||
| LICENSE | ||
| pylintrc | ||
| Ray.sln | ||
| README.md | ||
Ray
Ray is an experimental distributed execution engine. It is under development and not ready to be used.
The goal of Ray is to make it easy to write machine learning applications that run on a cluster while providing the development and debugging experience of working on a single machine.
View the documentation.