Overview
arcoPolo is an advanced reinforcement learning (RL) framework designed to facilitate the fast, accessible, and reproducible training of multiple artificial intelligence (AI) agents through RL. It improves upon existing techniques to create a robust system that allows agents to learn and adapt efficiently across different environments and scenarios. This approach to learning enables the training of generalized agents capable of zero shot learning.
Reinforcement Learning
Innnovation
Human AI Collaboration
Highlights
Overview
Problem: Multi-agent reinforcement learning was computationally intractable, but also necessary for efficient coordination of autonomous teams
Solution: MarcoPolo simplifies this process and makes many of the training components modifiable where they previously were fixed.
End Users: DRM 1-3 Space Operations in USAF/USSF
Core Process
Optimize: Train agents to perform better in their respective environments.
Transfer: Move agents between environments to identify top performers and facilitate cross-learning.
Evolve: Create new environments based on novelty and complexity, retiring older ones to keep the training dynamic and challenging.
Advantages:
Modularity:
MarcoPolo’s design allows different algorithms, environments, and backends to be easily integrated with existing systems.
Scalability:
Capable of handling large-scale training tasks, making it suitable for more practical applications involving multiple agents.
Flexibility:
Supports multi-agent simulations, enabling complex interactions and cooperative learning among agents.
Reproducibility:
MarcoPolo is seed safe, enabling accurate reproduction of simulations of interest and creating checkpoints during training regimen.
Download Whitepaper
Real-World Use
MarcoPolo has been utilized by several research institutions such as UC Berkeley, Virginia Tech, AFRL, and the University of Dayton Research Institute, showcasing its effectiveness and adaptability in various research and development scenarios.
Its ability to increase the accessibility of multi-agent automated curriculum learning makes it a valuable tool for researchers and developers alike in pushing the boundaries of AI capabilities. For more detailed information and to access the open-source project, visit the link below.
Marco Polo Github Repository
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