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

Partially Detected Intelligent Traffic Signal Control: Environmental Adaptation