Difference between revisions of "Silo"
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| − | + | '''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. | ||
| + | |||
| + | The frameworks will be made available under a permissive open-source license. | ||
| + | |||
| + | ==== 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. | ||
| + | |||
| + | __NOTOC__ __NOEDITSECTION__ | ||
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| + | {{Template:Bot Categorizer|author=Xor|isMega=true|isOneOnOne=true|isMelee=false|isOpenSource=false|extends=Interface}} | ||
Latest revision as of 02:50, 17 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.
The frameworks will be made available under a permissive open-source license.
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.
