Locked In: Multi-Objective RL for the AI Data Center Build-Out

The paper introduces a multi-objective reinforcement learning framework for planning AI data center build-outs, treating the long-term infrastructure decisions as a tiered Markov decision process. It proposes a preference-conditioned agent that can generate policies balancing cost, carbon, water, and reliability, and validates the approach on real site data across different commitment regimes. The study also presents a lightweight counterfactual rollout model to assess long-horizon dynamics and releases the associated environment, agents, and evaluation tools.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• agents
• reinforcement-learning