POMDPs.jl: a framework for sequential decision making under uncertainty. POMDPs.jl is an open-source framework for solving Markov decision processes (MDPs) and partially observable MDPs (POMDPs). POMDPs.jl allows users to specify sequential decision making problems with minimal effort without sacrificing the expressive nature of POMDPs, making this framework viable for both educational and research purposes. It is written in the Julia language to allow flexible prototyping and large-scale computation that leverages the high-performance nature of the language. The associated JuliaPOMDP community also provides a number of state-of-the-art MDP and POMDP solvers and a rich library of support tools to help with implementing new solvers and evaluating the solution results. The most recent version of POMDPs.jl, the related packages, and documentation can be found at https://github.com/ JuliaPOMDP/POMDPs.jl.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Choudhury, Shushman; Gupta, Jayesh K.; Morales, Peter; Kochenderfer, Mykel J.: Scalable online planning for multi-agent MDPs (2022)
- Minh Bui, George Giovanis, Mo Chen, Arrvindh Shriraman: OptimizedDP: An Efficient, User-friendly Library For Optimal Control and Dynamic Programming (2022) arXiv
- Robert J. Moss: POMDPStressTesting.jl: Adaptive Stress Testing for Black-Box Systems (2021) not zbMATH
- Tomin, N. V.: The concept of constructing an artificial dispatcher intelligent system based on deep reinforcement learning for the automatic control system of electric networks (2020)