Imprecise Probabilities Meet Partial Observability: Game Semantics for Robust POMDPs
Authors: Eline M. Bovy, Marnix Suilen, Sebastian Junges, Nils Jansen
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We expand the theoretical understanding of RPOMDPs by showing that 1) different assumptions on the uncertainty sets affect optimal policies and values; 2) RPOMDPs have a partially observable stochastic game (POSG) semantic; and 3) the same RPOMDP with different assumptions leads to semantically different POSGs and, thus, different policies and values. These novel semantics for RPOMDPs give access to results for POSGs, studied in game theory; concretely, we show the existence of a Nash equilibrium. Finally, we classify the existing RPOMDP literature using our semantics, clarifying under which uncertainty assumptions these existing works operate. This paper sets out to clarify and expand the theoretical understanding of RPOMDPs. |
| Researcher Affiliation | Academia | 1Radboud University, The Netherlands 2Ruhr-University Bochum, Germany |
| Pseudocode | No | The paper contains no pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The extended version of this paper, with all the appendices, can be found at [Bovy et al., 2024]. |
| Open Datasets | No | The paper is theoretical and does not involve training on datasets. |
| Dataset Splits | No | The paper is theoretical and does not discuss training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe hardware specifications for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |