Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs
Authors: Philipp Robbel, Frans Oliehoek, Mykel Kochenderfer
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experimental Evaluation We evaluate our methods on undirected disease propagation graphs with 30 and 50 nodes. For the first round of experiments, we contrast runtimes of the normal VE/ALP method (where possible) with those that exploit anonymous influence . We then consider a disease control problem with 25 agents in a densely connected 50-node graph that cannot be solved with the normal ALP. |
| Researcher Affiliation | Academia | Philipp Robbel MIT Media Lab Cambridge, MA, USA Frans A. Oliehoek University of Amsterdam University of Liverpool Mykel J. Kochenderfer Stanford University Stanford, CA, USA |
| Pseudocode | No | The paper describes methods like AUGMENT and REDUCE in prose and mathematical notation within Section 4 but does not present them in a structured pseudocode or algorithm block format. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing its source code or a link to a code repository for the methodology described. |
| Open Datasets | No | We use graph-tool (Peixoto 2014) to generate 10 random graphs with an out-degree k sampled from P(k) 1/k, k [1, 10]. The paper describes how the graphs were generated, but does not provide concrete access information (link, DOI, specific citation) for the generated datasets themselves. |
| Dataset Splits | No | The paper evaluates mean returns from 50 randomly sampled starting states after 200 steps of policy simulation, but it does not provide specific training/validation/test dataset splits (e.g., percentages or counts) or refer to standard predefined splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU/CPU models, memory, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions using 'graph-tool (Peixoto 2014)' for generating graphs, but it does not provide specific version numbers for software dependencies or libraries used in the experimental setup beyond this citation. |
| Experiment Setup | Yes | In all experiments, we use indicator functions IXi, I Xi on each state variable (covering the two valid instantiations {healthy, infected}) as the basis set H in the (RR-)ALP. We use identical transmission and node recovery rates throughout the graph, β = 0.6, δ = 0.3. Action costs are set to λ1 = 1 and infection costs to λ2 = 50. All experiments use the identical greedy elimination heuristic for both VE and RR-VE, which minimizes the scope size of intermediate terms at the next iteration. |