No project description provided
Project description
Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement learning as a Service (RaaS) for real-world resource optimization.
Home-page: https://github.com/microsoft/maro Author: Arthur Jiang Author-email: shujia.jiang@microsoft.com License: MIT License Project-URL: Code, https://github.com/microsoft/maro Project-URL: Issues, https://github.com/microsoft/maro/issues Project-URL: Documents, https://maro.readthedocs.io/en/latest Description: UNKNOWN Keywords: citi-bike,inventory-management,operations-research,reinforcement-learning,resource-optimization,simulator Platform: Windows Platform: Linux Platform: macOS Classifier: Programing Language :: Python Classifier: Programing Language :: Python :: 3 Requires-Python: >=3.6,<3.8
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distributions
Hashes for pymaro-0.1.1a4-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 20f45f0756c5e0e7cbcf438cf60a61f15ea5382aeed39d6ab98c38115bc3f0f5 |
|
MD5 | fdc08fefba8a552d59aa3ecd545abb53 |
|
BLAKE2b-256 | f2cd46e3e147886288fb467d5ec4fd7cfe3a63085e44b3c6565b0f79708814b0 |
Hashes for pymaro-0.1.1a4-cp37-cp37m-manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6b614c21693c239454b4e38d308fa17c59d62c55a20e4475d0b97951783baa01 |
|
MD5 | 32e7cc88574b2fae27d5571366fc2132 |
|
BLAKE2b-256 | 5d43023827e6aa642a7dcd248adbf37b53db513f571628dde54bbc2bf610a8f6 |
Hashes for pymaro-0.1.1a4-cp37-cp37m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5278528fe5eae8edbd86c67d46ce8f687fcbdf091880cd3d39e1ae539c243392 |
|
MD5 | edadf7e3b03a93179f0c3836d550660b |
|
BLAKE2b-256 | d47b411c3ff3eeaf460c722ac4c158b4151b9bd2bf9ec51b6270c35c9f935db7 |
Hashes for pymaro-0.1.1a4-cp37-cp37m-macosx_10_14_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 05c2632d216cae7219ac9fabdfd4fe08b6137d2b7fdaf2db1b17f12c95d72ea9 |
|
MD5 | 3e1c2e592062e98d0244d21baf118c33 |
|
BLAKE2b-256 | 1df071ad4880e749ac7840be50b2388137749bdc77b27855c63e9282176de1bd |
Hashes for pymaro-0.1.1a4-cp36-cp36m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c1100436b898355fad9d0edfb07967c33f631533e5c7d69028143240c6de9876 |
|
MD5 | e4f6377d3289955d904cca0309ea9461 |
|
BLAKE2b-256 | dd89797a2c69b419bddb56692a882da2382e3012999693ca3c66fc0b48fdb68a |
Hashes for pymaro-0.1.1a4-cp36-cp36m-manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c19deecc1db3d83596590f4b877acea1ef804d0ac77d00ca2a24baf92019563c |
|
MD5 | fed2d0f666579761249817520a405268 |
|
BLAKE2b-256 | ef15a82d38f3a3521215606403ca87af92ff8f5811067c7e1779a0ab7bd717b3 |
Hashes for pymaro-0.1.1a4-cp36-cp36m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9c7c6ee2fd36c2c4b92b0fa92a80985f0ade27fea3e8a233f1cbc58293d1b48a |
|
MD5 | 65c3edf7b0e6c303c0aa7cdd4a88a391 |
|
BLAKE2b-256 | 67b54e78d3345f718350ccd2d1447a5d625274ce599d47ace01d35ef2168c566 |
Hashes for pymaro-0.1.1a4-cp36-cp36m-macosx_10_14_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 005cb054e1598fbf9676a6774a54689ae104acdf15c7f54582951958d7a98d6d |
|
MD5 | e96e39ccfbad1b72838d1daa91e5b1a9 |
|
BLAKE2b-256 | 88ddbd70d5bac664d7c9d1ef327ef1e19ae48f2c6c162bcc47e7cdad89c291cb |