Constrained Risk-Averse Markov Decision Processes
Authors: Mohamadreza Ahmadi, Ugo Rosolia, Michel D. Ingham, Richard M. Murray, Aaron D. Ames11718-11725
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical Experiments In this section, we evaluate the proposed methodology with a numerical example. In addition to the traditional total expectation, we consider two other coherent risk measures, namely, CVa R and EVa R. All experiments were carried out using a Mac Book Pro with 2.8 GHz Quad-Core Intel Core i5 and 16 GB of RAM. The resultant linear programs and DCPs were solved using CVXPY (Diamond and Boyd 2016) with DCCP (Shen et al. 2016) add-on in Python. |
| Researcher Affiliation | Collaboration | 1California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125 2NASA Jet Propulsion Laboratory, 4800 Oak Grove Dr, Pasadena, CA 91109. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the authors' implementation code is open-source or publicly available. |
| Open Datasets | No | The paper describes a simulated 'grid world' for its experiments, but it does not specify or provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes numerical experiments and Monte Carlo simulations but does not specify any train/validation/test dataset splits. |
| Hardware Specification | Yes | All experiments were carried out using a Mac Book Pro with 2.8 GHz Quad-Core Intel Core i5 and 16 GB of RAM. |
| Software Dependencies | No | The paper mentions using 'CVXPY' and 'DCCP add-on in Python' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The action set available to the robot is Act = {E, W, N, S, NE, NW, SE, SW}, i.e., diagonal moves are allowed. ... The discount factor is γ = 0.95. ... we set ε = 0.15 for CVa R and EVa R coherent risk measures. The fuel budget (constraint bound β) was set to 50, 10, and 200 for the 10 10, 15 15, and 20 20 grid-worlds, respectively. |