SMAC

A new AI-driven approach integrating large language models into multi-objective Bayesian optimization (MOBO) has outperformed state-of-the-art methods, achieving a mean normalized hypervolume of 0.971…

Researchers introduced HyPOLE, a framework for multi-agent reinforcement learning under partial observation that uses hyperproperty specifications in HyperLTL to guide policy synthesis. The framework …

Researchers introduced TRIDENT, the first multi-agent reinforcement learning framework that co-designs three components to cancel biases from hybrid discrete-continuous actions, safety constraints, an…