  Partially Detected Intelligent Traffic Signal Control (PD-ITSC) systems that
can optimize traffic signals based on limited detected information could be a
cost-efficient solution for mitigating traffic congestion in the future. In
this paper, we focus on a particular problem in PD-ITSC - adaptation to
changing environments. To this end, we investigate different reinforcement
learning algorithms, including Q-learning, Proximal Policy Optimization (PPO),
Advantage Actor-Critic (A2C), and Actor-Critic with Kronecker-Factored Trust
Region (ACKTR). Our findings suggest that RL algorithms can find optimal
strategies under partial vehicle detection; however, policy-based algorithms
can adapt to changing environments more efficiently than value-based
algorithms. We use these findings to draw conclusions about the value of
different models for PD-ITSC systems.
