Difference between revisions of "Silo"
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| − | + | '''Silo''' is a newly developed robot, engineered using advanced reinforcement learning and deep learning techniques. | |
| + | |||
| + | === 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. | ||
| + | |||
| + | === Components === | ||
| + | |||
| + | * 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. | ||
Revision as of 17:10, 10 January 2024
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- Version History
| Silo | |
| Author(s) | Xor |
| Extends | AdvancedRobot |
Silo is a newly developed robot, engineered using advanced reinforcement learning and deep learning techniques.
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.
Components
- 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.
