López Villanueva, Ignacio: Viability‑Constrained Reinforcement Learning via Homeostatic State Augmentation

This work introduces a reinforcement learning framework in which the agent’s state is augmented with an internal viability variable representing structural and computational homeostasis. The resulting viability‑augmented MDP incorporates a logarithmic barrier in the reward, allowing safety to emerge directly from the agent’s internal dynamics rather than from external constraints. We prove that the associated Bellman operator remains a contraction under standard boundedness assumptions, ensuring