Symbolic Dynamic Programming for Continuous State MDPs with Linear Program Transitions
Authors: Jihwan Jeong, Parth Jaggi, Scott Sanner
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now apply our methodology for exact SDP with LP transitions to SIMPLE TRAFFIC MANAGEMENT, RESERVOIR MANAGEMENT, and BANDWIDTH OPTIMIZATION.3 We also compare to approaches that discretize the state space of the DC-MDP as discussed in Section 1. [...] Figure 4 shows the time and space complexity of approximate solutions along with that of the symbolic solutions. |
| Researcher Affiliation | Academia | Jihwan Jeong 1 , Parth Jaggi 1 , Scott Sanner1,2 1Department of Mechanical & Industrial Engineering, University of Toronto, Canada 2Vector Institute, Toronto, Canada. jhjeong@mie.utoronto.ca, parth.jaggi@mail.utoronto.ca, ssanner@mie.utoronto.ca |
| Pseudocode | No | The paper describes symbolic operations and algorithms using mathematical notation and textual explanations (e.g., Section 3.2, 3.3), but it does not contain a distinct pseudocode block or a clearly labeled algorithm section. |
| Open Source Code | Yes | Implementations can be found at https://github.com/jihwan-jeong/xadd-inference/. |
| Open Datasets | No | The paper describes three problem domains (SIMPLE TRAFFIC MANAGEMENT, RESERVOIR MANAGEMENT, BANDWIDTH OPTIMIZATION) and defines problem instances with parameters. However, it does not provide concrete access information (e.g., URL, DOI, repository, or formal citation for a specific public dataset) for data used in experiments. |
| Dataset Splits | No | The paper focuses on developing an exact symbolic solution for MDPs and compares it to discretized approximations. It does not describe training, validation, or test dataset splits in the context of machine learning model development. |
| Hardware Specification | No | The paper discusses memory and time complexity but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper refers to its implementations (using XADD form) being available on GitHub, but it does not explicitly list specific software dependencies (e.g., libraries, frameworks, or solvers) along with their version numbers within the text. |
| Experiment Setup | No | The paper describes the mathematical formulations and parameters of the problem instances (e.g., road capacities, water levels). However, it does not provide details about typical experimental setup parameters such as hyperparameters, learning rates, batch sizes, or specific training configurations, as the approach is symbolic and exact rather than data-driven machine learning. |