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Batch Learning in Stochastic Dual Dynamic Programming

Daniel ┴vila(daniel.avila***at***uclouvain.be)
Anthony Papavasiliou(anthony.papavasiliou***at***uclouvain.be)
Nils L÷hndorf(nils.loehndorf***at***uni.lu)

Abstract: We consider the stochastic dual dynamic programming (SDDP) algorithm, which is a widely employed algorithm applied to multistage stochastic programming, and propose a variant using batch learning, a technique used with success in the reinforcement learning framework. We cast SDDP as a type of Q-learning algorithm and describe its application in both risk neutral and risk averse settings. We demonstrate the efficiency of the algorithm on a lost sales inventory control problem with lead times, as well as a real-world instance of the long-term planning problem of interconnected hydropower plants in Colombia. We find that the proposed technique is able to produce tighter optimality gaps in a shorter amount of time than conventional SDDP, including the PSR SDDP commercial software. We also find that parallel computation of SDDP backward passes benefit from batch learning.

Keywords: Stochastic programming, Dynamic programming, Reinforcement learning, SDDP, Parallel Computing

Category 1: Stochastic Programming

Category 2: Other Topics (Dynamic Programming )

Citation:

Download: [PDF]

Entry Submitted: 05/17/2021
Entry Accepted: 05/17/2021
Entry Last Modified: 05/17/2021

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