  Urban congestion remains a critical challenge, with traffic signal control
(TSC) emerging as a potent solution. TSC is often modeled as a Markov Decision
Process problem and then solved using reinforcement learning (RL), which has
proven effective. However, the existing RL-based TSC system often overlooks
imperfect observations caused by degraded communication, such as packet loss,
delays, and noise, as well as rare real-life events not included in the reward
function, such as unconsidered emergency vehicles. To address these
limitations, we introduce a novel integration framework that combines a large
language model (LLM) with RL. This framework is designed to manage overlooked
elements in the reward function and gaps in state information, thereby
enhancing the policies of RL agents. In our approach, RL initially makes
decisions based on observed data. Subsequently, LLMs evaluate these decisions
to verify their reasonableness. If a decision is found to be unreasonable, it
is adjusted accordingly. Additionally, this integration approach can be
seamlessly integrated with existing RL-based TSC systems without necessitating
modifications. Extensive testing confirms that our approach reduces the average
waiting time by $17.5\%$ in degraded communication conditions as compared to
traditional RL methods, underscoring its potential to advance practical RL
applications in intelligent transportation systems. The related code can be
found at \url{https://github.com/Traffic-Alpha/iLLM-TSC}.
