A two-stage actor-critic RL algorithm learns deterministic equilibrium policies for general time-inconsistent control problems by combining DPG on an auxiliary time-consistent problem with fixed-point iteration on auxiliary functions.
Continuous-time q-learning for jump-diffusion models under tsallis entropy
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
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An actor-critic framework built on a time-inhomogeneous little q-function and conditional normalizing flows serves as a mesh-free solver for entropy-regularized jump-diffusion control problems and stochastic games.
The paper introduces an entropy-regularized RL framework deriving exploratory weakly-coupled HJBI equations and using neural networks to approximate value functions for high-dimensional LQ-SDGs under Markov regime switching.
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Entropy-Regularized Reinforcement Learning for Linear-Quadratic Stackelberg Differential Games in Regime-Switching Diffusion Models
The paper introduces an entropy-regularized RL framework deriving exploratory weakly-coupled HJBI equations and using neural networks to approximate value functions for high-dimensional LQ-SDGs under Markov regime switching.