Difference between revisions of "Silo"
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| − | '''Silo''' | + | '''Silo''' represents the forefront of robocode engineering, being meticulously crafted using cutting-edge reinforcement learning and deep learning methodologies. It is presently in the throes of active development. |
=== Vision === | === Vision === | ||
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* 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. | ||
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| + | {{Template:Bot Categorizer|author=Xor|isMega=true|isOneOnOne=true|isMelee=false|isOpenSource=false|extends=Interface}} | ||
Revision as of 17:18, 10 January 2024
- Sub-pages:
- Version History
| Silo | |
| Author(s) | Xor |
| Extends | AdvancedRobot |
Silo represents the forefront of robocode engineering, being meticulously crafted using cutting-edge reinforcement learning and deep learning methodologies. It is presently in the throes of active development.
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.
