thousands, or even millions of state variables. Unfortunately, applying reinforcement learning algorithms to handle complex tasks becomes more and more challenging as the number of state variables increases. In this paper, we build on the concept of influence-based abstraction which tries to tackle such scalability issues by decomposing large systems into small regions. We explore this method in the context of deep reinforcement learning, showing that by keeping track of a small set of variables in the history of previous actions and observations we can learn policies that can effectively control a local region in the global system.
|Title of host publication||AAMAS Workshop on Adaptive Learning Agents (ALA)|
|Place of Publication||Montreal, Canada|
|Number of pages||8|
|Publication status||Published - 13 May 2019|
|Event||ALA 2019 - Workshop at AAMAS 2019 - Montreal, Canada|
Duration: 13 May 2019 → 14 May 2019
|Workshop||ALA 2019 - Workshop at AAMAS 2019|
|Period||13/05/19 → 14/05/19|
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.
- Reinforcement Learning
- Influence-based abstraction