Bellman's principle of optimality and deep reinforcement learning for time-varying tasks

DOI10.1080/00207179.2021.1913516zbMath1500.93144OpenAlexW3146773041MaRDI QIDQ5043501

Alessandro Giuseppi, Antonio Pietrabissa

Publication date: 6 October 2022

Published in: International Journal of Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207179.2021.1913516

zbMATH Keywords

finite-horizon optimal controlBellman's principlemodel-free controldeep reinforcement learning

Mathematics Subject Classification ID

Optimal stochastic control (93E20) Markov and semi-Markov decision processes (90C40)

Related Items (1)

A Lyapunov-based version of the value iteration algorithm formulated as a discrete-time switched affine system

Uses Software

• OpenAI Gym
• PyTorch
• GitHub

Cites Work

• An iterative adaptive dynamic programming method for solving a class of nonlinear zero-sum differential games
• Adaptive dynamic programming for discrete-time linear quadratic regulation based on multirate generalised policy iteration
• Allocating resources via price management systems: a dynamic programming-based approach
• Modelling and solving resource allocation problems via a dynamic programming approach
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