Foundation World Models for Agents that Learn, Verify, and Adapt Reliably Beyond Static Environments | ScienceToStartup | ScienceToStartup

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References (58)

[1]

Shields for Safe Reinforcement Learning

2025Bettina Könighofer, Roderick Bloem et al.

[2]

ARM-FM: Automated Reward Machines via Foundation Models for Compositional Reinforcement Learning

2025Roger Creus Castanyer, Faisal Mohamed et al.

[3]

Training Agents Inside of Scalable World Models

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"Develop robust foundation world models for autonomous agents to reliably adapt and learn in dynamic environments."

Agents•Score: 3•View PDF ↗

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Foundation World Models for Agents that Learn, Verify, and Adapt Reliably Beyond Static Environments

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References (58)

Founder's Pitch

"Develop robust foundation world models for autonomous agents to reliably adapt and learn in dynamic environments."

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Foundation World Models for Agents that Learn, Verify, and Adapt Reliably Beyond Static Environments

BUILDER'S SANDBOX

Build This Paper

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References (58)

Founder's Pitch

"Develop robust foundation world models for autonomous agents to reliably adapt and learn in dynamic environments."

Commercial Viability Breakdown

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