POMDPs.jl: A Framework for Sequential Decision Making under Uncertainty
Authors: Maxim Egorov, Zachary N. Sunberg, Edward Balaban, Tim A. Wheeler, Jayesh K. Gupta, Mykel J. Kochenderfer
JMLR 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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 designed to support users in three different roles: 1) defining problems, 2) creating solvers, and 3) running experiments. |
| Researcher Affiliation | Academia | Maxim Egorov EMAIL Zachary N. Sunberg EMAIL Edward Balaban EMAIL Tim A. Wheeler EMAIL Jayesh K. Gupta EMAIL Mykel J. Kochenderfer EMAIL Department of Aeronautics and Astronautics Stanford University Stanford, CA 94305, USA |
| Pseudocode | No | The paper includes code snippets in Section 4 for defining problem types, solver policies, and simulation functions, but these are concrete Julia code examples for illustration, not structured pseudocode or clearly labeled algorithm blocks presenting a new method. |
| Open Source Code | Yes | POMDPs.jl is an open-source framework for solving Markov decision processes (MDPs) and partially observable MDPs (POMDPs)... The most recent version of POMDPs.jl, the related packages, and documentation can be found at https://github.com/Julia POMDP/POMDPs.jl. |
| Open Datasets | No | The paper introduces a framework for sequential decision making but does not conduct experiments on specific datasets. It references problem types like 'aircraft collision avoidance' or 'Tiger POMDP' as examples of what the framework can solve, but it does not use, or provide access information for, any specific datasets within the scope of this paper's contribution. |
| Dataset Splits | No | The paper describes a software framework and its capabilities. It does not conduct experiments using specific datasets, and therefore, no information regarding dataset splits is provided. |
| Hardware Specification | No | The paper discusses a software framework and its general computational capabilities but does not provide specific hardware details (like GPU/CPU models or memory specifications) used for running any experiments within the paper. |
| Software Dependencies | No | The paper states that the framework is 'written in the Julia language (Bezanson et al., 2012)' but does not provide specific version numbers for Julia or any other key software dependencies or libraries used in the development or demonstration of the framework. |
| Experiment Setup | No | The paper describes the general concepts and use case examples for setting up problems and solvers within the POMDPs.jl framework, including illustrative code for a simulation loop. However, it does not present details of specific experiments conducted by the authors, nor does it provide concrete hyperparameter values, training configurations, or system-level settings for any such experiments. |