maro
VMAgent
| maro | VMAgent | |
|---|---|---|
| 9 | 1 | |
| 816 | 75 | |
| 1.7% | - | |
| 3.5 | 1.4 | |
| 2 months ago | about 1 year ago | |
| Python | Python | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
maro
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Headstart for multi-container optimization problem.
Yes, I have actually. Some of them which i could find out were: https://github.com/tryton/tryton/tree/main https://pypi.org/project/pyShipping-python3/ https://github.com/microsoft/maro https://github.com/yat-co/yat-trailer-loading https://github.com/duyet/openerp-6.1.1
- maro: NEW Deep Learning And Reinforcement Learning - star count:609.0
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What's the outlook of Reinforcement Learning?
As far as current SOTA applications, you can just Google it and find plenty of examples of RL being used outside the realm of games. Video/board games offer a nice domain for research in RL, but the underlying algorithms can be (and have been) applied to plenty of domains outside of this. A big one, currently, is robotics. Another example is resource optimization, which is probably currently being developed, if not used, in a lot of technical domains. As u/daddabarba pointed out, RL can also be used in other areas of AI, like text generation.
VMAgent
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Huawei Research Introduces ‘VMAgent’: A Platform for Exploiting Reinforcement Learning (RL) on Virtual Machine (VM) Scheduling Tasks
In a recent study, researchers from Huawei Cloud’s Multi-Agent Artificial Intelligence Lab and Algorithm Innovation Lab suggested VMAgent, a unique VM scheduling simulator based on real data from Huawei Cloud’s actual operation situations. VMAgent seeks to replicate the scheduling of virtual machine requests across many servers (allocating and releasing CPU and memory resources). It creates virtual machine scheduling scenarios using real-world system design, such as fading, recovering, and expanding virtual machines. Only requests can be allocated in the fading situation, whereas the recovering scenario permits both allocating and releasing VM resources.
What are some alternatives?
openerp-6.1.1
garage - A toolkit for reproducible reinforcement learning research.
pyEnigma - Python Enigma cypher machine simulator.
agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
agents-aea - A framework for autonomous economic agent (AEA) development
coo - Schedule Twitter updates with easy
ai-economist - Foundation is a flexible, modular, and composable framework to model socio-economic behaviors and dynamics with both agents and governments. This framework can be used in conjunction with reinforcement learning to learn optimal economic policies, as done by the AI Economist (https://www.einstein.ai/the-ai-economist).
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
palletier - Palletier is a Python implementation of the solution for the distributer's pallet packing problem
oncall - Oncall is a calendar tool designed for scheduling and managing on-call shifts. It can be used as source of dynamic ownership info for paging systems like http://iris.claims.
blender-quadcopter-fpv - Quadcopter FPV Simulator for blender to capture epic footage
Mava - 🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX