Difference between revisions of "Silo"
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Hand-crafted strategies have their merits, but algorithms that dynamically adapt to evolving distributions are superior. The ultimate objective of Silo is to develop a suite of self-evolving algorithms, capable of outperforming all competitors through continual data updates alone. | Hand-crafted strategies have their merits, but algorithms that dynamically adapt to evolving distributions are superior. The ultimate objective of Silo is to develop a suite of self-evolving algorithms, capable of outperforming all competitors through continual data updates alone. | ||
| − | === | + | === Development === |
| + | The development of Silo unfolds in two interdependent parts, evolving in tandem. | ||
| + | |||
| + | ==== Frameworks ==== | ||
* An advanced robot development framework designed for automated testing and experimentation, which ultimately progresses to self-programming capabilities. | * An advanced robot development framework designed for automated testing and experimentation, which ultimately progresses to self-programming capabilities. | ||
* A comprehensive framework for robot data collection, processing, and model training, tailored specifically for deep reinforcement learning techniques. This integrated system allows for optimization and evaluation of every component within a unified platform. | * A comprehensive framework for robot data collection, processing, and model training, tailored specifically for deep reinforcement learning techniques. This integrated system allows for optimization and evaluation of every component within a unified platform. | ||
| − | === Models === | + | ==== Models ==== |
* A cohesive teacher network adept at learning targeting, wave surfing, and energy management in an end-to-end manner. | * A cohesive teacher network adept at learning targeting, wave surfing, and energy management in an end-to-end manner. | ||
* Multiple compact networks designed to distill the knowledge acquired and unearthed by the teacher network, ideal for environments with constrained computational resources. | * Multiple compact networks designed to distill the knowledge acquired and unearthed by the teacher network, ideal for environments with constrained computational resources. | ||
Revision as of 16:15, 10 January 2024
- Sub-pages:
- Version History
| Silo | |
| Author(s) | Xor |
| Extends | AdvancedRobot |
Silo is a newly developed robot, engineered using advanced reinforcement learning and deep learning techniques.
Contents
Vision
Hand-crafted strategies have their merits, but algorithms that dynamically adapt to evolving distributions are superior. The ultimate objective of Silo is to develop a suite of self-evolving algorithms, capable of outperforming all competitors through continual data updates alone.
Development
The development of Silo unfolds in two interdependent parts, evolving in tandem.
Frameworks
- An advanced robot development framework designed for automated testing and experimentation, which ultimately progresses to self-programming capabilities.
- A comprehensive framework for robot data collection, processing, and model training, tailored specifically for deep reinforcement learning techniques. This integrated system allows for optimization and evaluation of every component within a unified platform.
Models
- A cohesive teacher network adept at learning targeting, wave surfing, and energy management in an end-to-end manner.
- Multiple compact networks designed to distill the knowledge acquired and unearthed by the teacher network, ideal for environments with constrained computational resources.
