Planning with Hidden Parameter Polynomial MDPs
Authors: Clarissa Costen, Marc Rigter, Bruno Lacerda, Nick Hawes
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate two domains and empirically show that the polynomial, closed-form, belief representation results in better plans than a sampling-based belief representation. |
| Researcher Affiliation | Academia | Clarissa Costen, Marc Rigter, Bruno Lacerda, Nick Hawes Oxford Robotics Institute, University of Oxford {clarissa, mrigter, bruno, nickh}@robots.ox.ac.uk |
| Pseudocode | Yes | Algorithm 1: Bayes-Adaptive Monte Carlo Planning |
| Open Source Code | No | The paper does not provide an explicit statement or link to the open-source code for the methodology described. |
| Open Datasets | Yes | The forecast for the longitudinal and latitudinal velocity of the water is known ahead of time, using data from Copernicus (2022). |
| Dataset Splits | No | The paper describes experimental simulations and sampling of true parameters, but it does not specify explicit training, validation, or test dataset splits for a fixed dataset, as the data is generated through interaction with the simulated environments. |
| Hardware Specification | Yes | We ran the simulations on Linux OS with an Intel Core i9-11900H processor and 16GB of RAM. We ran the trials on Cent OS with an Intel Xeon Platinum 8268 CPU at 2.90 GHz Processor, with 16GB of RAM. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers (e.g., programming languages, libraries, or solvers with their versions) that would be needed to replicate the experiment. |
| Experiment Setup | Yes | We set T to 20 seconds. We also varied the size of the set of samples used to represent the posterior distribution, M, for BAMCP-G and -PF. applied dimension reduction to HP2-MDP, where the highest degree of the polynomial was fixed to 12. |