TY  - JOUR
PY  - 2023//
TI  - Deep reinforcement learning for traffic signal control
JO  - Transportation research procedia
A1  - Skuba, Michal
A1  - Janota, Aleš
A1  - Kuchár, Pavol
A1  - Malobický, Branislav
SP  - 954
EP  - 958
VL  - 74
IS  - 
N2  - The increasing demand for mobility in cities and beyond presents challenges for traffic engineering. As a solution to these challenges, we can use the power of artificial intelligence and reinforcement learning. These scientific branches can enable us to use the current infrastructure more efficiently, thus reducing the environmental impact and increasing the comfort of drivers. In this paper, using the Ingolstadt and Cologne benchmarks from RESCO, we compare our agent, which is based on Perceiver with IDQN (Independent Deep Q-Network), to the conventional method of Max-pressure and random times for green signals.<p />  <p>Language: en</p>
LA  - en
SN  - 2352-1465
UR  - http://dx.doi.org/10.1016/j.trpro.2023.11.230
ID  - ref1
ER  -