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A Method to Improve the Performance of Reinforcement Learning Based on the Y Operator for a Class of Stochastic Differential Equation-Based Child-Mother Systems

arXiv 2311.04014 ↗First posted 7 Nov 2023cs.AI math.OCcited by 0

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Abstract (arXiv)

This paper introduces a novel operator, termed the Y operator, to elevate control performance in Actor-Critic(AC) based reinforcement learning for systems governed by stochastic differential equations(SDEs). The Y operator ingeniously integrates the stochasticity of a class of child-mother system into the Critic network's loss function, yielding substantial advancements in the control performance of RL algorithms.Additionally, the Y operator elegantly reformulates the challenge of solving partial differential equations for the state-value function into a parallel problem for the drift and diffusion functions within the system's SDEs.A rigorous mathematical proof confirms the operator's validity.This transformation enables the Y Operator-based Reinforcement Learning(YORL) framework to efficiently tackle optimal control problems in both model-based and data-driven systems.The superiority of YORL is demonstrated through linear and nonlinear numerical examples showing its enhanced performance over existing methods post convergence.

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References · 16

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
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  11. 2018Exploration versus exploitation in reinforcement learning: a stochastic control approachcited by 17
  12. 2019Deep Neural Network Framework Based on Backward Stochastic Differential Equations for Pricing and Hedging American Options in High Dimensionscited by 14
4 more
  1. 2021Neural network stochastic differential equation models with applications to financial data forecastingcited by 14
  2. 2023How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equationscited by 10
  3. 2021Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equationscited by 4
  4. 2019Incremental Reinforcement Learning --- a New Continuous Reinforcement Learning Frame Based on Stochastic Differential Equation methodscited by 2

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