Derives an explicit Gâteaux policy-gradient formula for entropy-regularized continuous-time mean-field control using the value function and cylindrical representations, then builds a model-based actor-critic scheme with PDE well-posedness analysis.
PhiBE: A PDE-based Bellman equation for continuous time policy evaluation
4 Pith papers cite this work. Polarity classification is still indexing.
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Introduces MF-PhiBE to perform continuous-time mean-field RL from discrete data, with O(Δt) consistency and O((Δt)^2) accuracy in the LQ case.
Introduces a new Q-function definition for continuous-time RL and convergent off-policy algorithms under linear function approximation in model-based and model-free settings.
Derives quantitative convergence rates for the gap between optimal policies from regularized discrete-time Bellman equations and true optimal controls in underlying continuous-time stochastic problems.
citing papers explorer
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Policy Gradient for Continuous-Time Mean-Field Control
Derives an explicit Gâteaux policy-gradient formula for entropy-regularized continuous-time mean-field control using the value function and cylindrical representations, then builds a model-based actor-critic scheme with PDE well-posedness analysis.
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Mean-Field PhiBE: Continuous-Time Mean-Field Reinforcement Learning from Discrete-Time Data
Introduces MF-PhiBE to perform continuous-time mean-field RL from discrete data, with O(Δt) consistency and O((Δt)^2) accuracy in the LQ case.
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PhiBE-Q-Learning: Bridging Off-Policy Reinforcement Learning and Continuous-Time Control
Introduces a new Q-function definition for continuous-time RL and convergent off-policy algorithms under linear function approximation in model-based and model-free settings.
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Discretization error from regularized Reinforcement Learning to continuous-time stochastic control
Derives quantitative convergence rates for the gap between optimal policies from regularized discrete-time Bellman equations and true optimal controls in underlying continuous-time stochastic problems.