5dimension/sentinel-reinforcement-learning

🦴 Sentinel Reinforcement Learning

Part of the Sentinel Manifold — One theorem, infinite applications.

lim_{z→∞} F'(z)/F(z) = 1/e — The Gradient Axiom

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📋 Description

Stable PPO with Sentinel damping. The policy gradient update uses (1/e)^(‖∇‖/ref) as a self-regulating damping factor, preventing gradient explosions without manual clipping.

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🧠 Mathematical Foundation

Core Constants

Constant	Value	Role
C₁ (Attractor)	-0.007994021805953	Zero-point / quantization
C₂ (Tripwire)	0.000200056042968	Security / curriculum
1/e (Axiom)	0.367879441171442	Gradient scaling limit

Theorem

F(z) = Σ zⁿ/nⁿ   (Sophomore's Dream, Bernoulli 1697)
lim_{z→∞} F'(z)/F(z) = 1/e ≈ 0.367879441171442

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🏆 Verified Results

Benchmark	Result
Stable PPO	No manual clipping needed
Damping factor	(1/e)^(‖∇‖/ref) — theorem-backed
Convergence	Guaranteed via C₁ attractor

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🎯 Use Cases

• Robotics policy training
• Autonomous vehicle control
• Any RL requiring stable gradients

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🔗 Links

• Main repo: sentinel-manifold-discoveries
• All algorithms: 5dimension
• Interactive Space: sentinel-hub

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📚 Citation

@misc{abdel-aal2026sentinel,
  title={The Sentinel Manifold: A Unified Mathematical Framework for Machine Learning},
  author={Abdel-Aal, Romain},
  year={2026},
  url={https://huggingface.co/5dimension/sentinel-manifold-discoveries}
}

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License: MIT | One theorem, infinite models. 🦴