HPO enables unbiased policy optimization in hybrid action spaces by mixing differentiable simulation gradients with score-function estimates, outperforming PPO as continuous dimensions increase.
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Learned primal and dual maps conditioned on population summaries enable reliable coordination across composition shifts in large multi-agent systems, cutting forecast error 16-19% and violations 20-51% in a supply-chain case study.
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Policy Optimization in Hybrid Discrete-Continuous Action Spaces via Mixed Gradients
HPO enables unbiased policy optimization in hybrid action spaces by mixing differentiable simulation gradients with score-function estimates, outperforming PPO as continuous dimensions increase.
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Ready from Day 1: Population-Aware Coordination for Large-Scale Constrained Multi-Agent Systems
Learned primal and dual maps conditioned on population summaries enable reliable coordination across composition shifts in large multi-agent systems, cutting forecast error 16-19% and violations 20-51% in a supply-chain case study.