Agent's optimization in unique-contract principal-agent problem with adverse selection is recast as stochastic target problem, enabling principal's objective as stochastic optimal control with partial information and state constraints.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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QuantFPFlow uses quantum amplitude estimation in a Fokker-Planck RL framework to achieve O(1/ε) partition function estimation and reports improved global optimum discovery plus better scaling in continuous control tasks.
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Principal-agent problems with adverse selection: A stochastic target problem formulation
Agent's optimization in unique-contract principal-agent problem with adverse selection is recast as stochastic target problem, enabling principal's objective as stochastic optimal control with partial information and state constraints.
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QuantFPFlow: Quantum Amplitude Estimation for Fokker--Planck Policy Optimisation in Continuous Reinforcement Learning
QuantFPFlow uses quantum amplitude estimation in a Fokker-Planck RL framework to achieve O(1/ε) partition function estimation and reports improved global optimum discovery plus better scaling in continuous control tasks.