Reinforcement learning (RL) constitutes a promising solution for alleviating the problem of traffic congestion. In particular, deep RL algorithms have been shown to produce adaptive traffic signal controllers that outperform conventional systems. However, in order to be reliable in highly dynamic urban areas, such controllers need to be robust with the respect to a series of exogenous sources of uncertainty. In this paper, we develop an open-source callback-based framework for promoting the flexible evaluation of different deep RL configurations under a traffic simulation environment. With this framework, we investigate how deep RL-based adaptive traffic controllers perform under different scenarios, namely under demand surges caused by special events, capacity reductions from incidents and sensor failures. We extract several key insights for the development of robust deep RL algorithms for traffic control and propose concrete designs to mitigate the impact of the considered exogenous uncertainties.
|Title of host publication||Proceedings of the IEEE Intelligent Transportation Systems Conference (ITSC) 2019|
|Number of pages||8|
|Publication status||Published - 2019|
|Event||ITSC 2019 : The 22nd IEEE International Conference on Intelligent Transportation Systems - Conference Venue Cordis Hotel, Auckland, Auckland, New Zealand|
Duration: 27 Oct 2019 → 30 Oct 2019
|Location||Conference Venue Cordis Hotel, Auckland|
|Period||27/10/2019 → 30/10/2019|