Stochastic Market Games
Authors: Kyrill Schmid, Lenz Belzner, Robert Müller, Johannes Tochtermann, Claudia Linnhoff-Popien
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically find that the presence of markets can improve both the overall result and agent individual returns via their trading activities. |
| Researcher Affiliation | Academia | 1LMU Munich 2Technische Hochschule Ingolstadt |
| Pseudocode | Yes | pseudocode given in Algorithm 1 |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper introduces custom domains called 'Smartfactory' and 'Refinery' for evaluation and does not provide access information or citations for publicly available datasets. |
| Dataset Splits | No | The paper describes training processes and evaluation runs but does not specify train/validation/test dataset splits, percentages, or sample counts. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper refers to various algorithms and models (DQN, PPO, MADDPG) but does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | For the experiment we set R1 = 0.375 and R2 = 2.625...two independent Q-learners were trained over 25 independent runs for each level of conflict α, where each run consisted of 4000 consecutive steps. ... here we used p = 1.9. ... In all runs we set rhigh = 5, rlow = 1 tinactive = 8 in the Smartfactory and rhigh = 5, rlow = 0.02 in the Refinery. ... All settings were tested with 4, 8 and 16 agents... |