Bayesian Optimized Monte Carlo Planning
Authors: John Mern, Anil Yildiz, Zachary Sunberg, Tapan Mukerji, Mykel J. Kochenderfer11880-11887
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the effectiveness of BOMCP, we conducted a series of experiments on three distinct POMDPs. We evaluated the performance of BOMCP against the performance of POMCPOW and expert policies for each problem. For each experiment, we recorded the task score as well as the wall clock run time per-search to measure the computation cost. |
| Researcher Affiliation | Academia | John Mern,1 Anil Yildiz,1 Zachary Sunberg,2 Tapan Mukerji,3 and Mykel J. Kochenderfer1 1Stanford University, Department of Aeronautics and Astronautics, 496 Lomita Mall, Stanford, CA 94305 2University of Colorado Boulder, Department of Aerospace Engineering Sciences, 3775 Discovery Drive, Boulder, CO 80303 3Stanford University, Department of Energy Resources Engineering, 367 Panama Street, Stanford, CA 94305 |
| Pseudocode | Yes | Algorithm 1 Plan, Algorithm 2 Simulate, Algorithm 3 Bayesian Optimization |
| Open Source Code | Yes | Source code for BOMCP is available at https://github.com/sisl/BOMCP.jl. |
| Open Datasets | Yes | We used data from the Global Wind Atlas at the Altamont Pass wind farm, which covers an area of approximately 392 km2. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, and test splits for the datasets or simulation environments used. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper states, 'We implemented BOMCP and BOMCTS in Julia building upon the POMDPs.jl package (Egorov et al. 2017),' but does not provide specific version numbers for Julia or POMDPs.jl. |
| Experiment Setup | Yes | For all tests, the same values were used for hyper-parameters shared by BOMCP and POMCPOW such as Kaction and αaction. The initial vehicle state is sampled from a multivariate Gaussian with mean µ = (x = 0, y = 50, θ = 0, x = 0, y = 10, ω = 0). The action space is a three-dimensional continuous space defined by the tuple (T, Fx, δ). T is the main thrust which is in the range [0, 15]. Fx is the corrective thrust, which is in the range [ 5, 5] and δ [ 1, 1]. |