Multi-Agent Planning with Baseline Regret Minimization
Authors: Feng Wu, Shlomo Zilberstein, Xiaoping Chen
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on common benchmark problems confirm the benefits of the algorithm compared with the state-of-the-art approaches. |
| Researcher Affiliation | Academia | School of Computer Science and Technology, University of Science and Technology of China, CHN College of Information and Computer Sciences, University of Massachusetts Amherst, USA |
| Pseudocode | Yes | Algorithm 1: Iterative Belief Generation |
| Open Source Code | No | No statement about releasing open-source code for the described methodology is found. |
| Open Datasets | Yes | We empirically evaluated our algorithm on four common benchmark problems1 widely used in the DEC-POMDP literature: Broadcast Channel, Recycling Robots, Cooperative Box Pushing, and Meeting in a 3 3 Grid. 1http://masplan.org/problem_domains |
| Dataset Splits | No | The paper discusses running experiments on benchmark problems and problem instances, but does not specify training, validation, or test dataset splits. |
| Hardware Specification | Yes | Our algorithm (i.e., IBG-DP) was implemented in Java 1.8 and ran on a machine with 3.5GHz Intel Core i7 CPU and 8GB of RAM. |
| Software Dependencies | Yes | Our algorithm (i.e., IBG-DP) was implemented in Java 1.8 and ran on a machine with 3.5GHz Intel Core i7 CPU and 8GB of RAM. The MILP and LP were solved by IBM CPLEX 12.61. |
| Experiment Setup | Yes | The baseline policies that we used here are random policy trees with max Tree = 3. |