TY  - JOUR
PY  - 2023//
TI  - Deep reinforcement meta-learning and self-organization in complex systems: applications to traffic signal control
JO  - Entropy (Basel, Switzerland)
A1  - Korecki, Marcin
SP  - e982
EP  - e982
VL  - 25
IS  - 7
N2  - We studied the ability of deep reinforcement learning and self-organizing approaches to adapt to dynamic complex systems, using the applied example of traffic signal control in a simulated urban environment. We highlight the general limitations of deep learning for control in complex systems, even when employing state-of-the-art meta-learning methods, and contrast it with self-organization-based methods. Accordingly, we argue that complex systems are a good and challenging study environment for developing and improving meta-learning approaches. At the same time, we point to the importance of baselines to which meta-learning methods can be compared and present a self-organizing analytic traffic signal control that outperforms state-of-the-art meta-learning in some scenarios. We also show that meta-learning methods outperform classical learning methods in our simulated environment (around 1.5-2× improvement, in most scenarios). Our conclusions are that, in order to develop effective meta-learning methods that are able to adapt to a variety of conditions, it is necessary to test them in demanding, complex settings (such as, for example, urban traffic control) and compare them against established methods.<p />  <p>Language: en</p>
LA  - en
SN  - 1099-4300
UR  - http://dx.doi.org/10.3390/e25070982
ID  - ref1
ER  -