While the POMDP has proven to be a powerful framework to model and solve partially observable stochastic problems, it assumes ac- curate and complete knowledge of the environment. When such information is not available, as is the case in many real world appli- cations, one must learn such a model. The BA-POMDP considers the model as part of the hidden state and explicitly considers the uncertainty over it, and as a result transforms the learning problem into a planning problem. This model, however, grows exponentially with the underlying POMDP size, and becomes intractable for non- trivial problems. In this article we propose a factored framework, the FBA-POMDP that represents the model as a Bayes-Net, dras- tically decreasing the number of parameters required to describe the dynamics of the environment. We demonstrate that the our ap- proach allows solvers to tackle problems much larger than possible in the BA-POMDP.
|Title of host publication||Adaptive Learning Agents (ALA 2018)|
|Number of pages||6|
|Publication status||Published - 1 Jul 2018|
|Event||ALA 2018 - Workshop at the Federated AI Meeting 2018 - Stockholm, Sweden|
Duration: 14 Jul 2018 → 15 Jul 2019
|Conference||ALA 2018 - Workshop at the Federated AI Meeting 2018|
|Abbreviated title||ALA 2018|
|Period||14/07/18 → 15/07/19|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- refereed, workshop