Decentralized Multi-Agent intelligent traffic light controller based on a deep reinforcement learning model is developed and analysed with fixed and adaptive traffic light controllers. We integrate this model with MultiVeStA to check properties of the controller. We evaluate the performance of model trained with MultiVeStA against the model trained without MultiVestA. We analyse the result in terms of waiting time and probability of queries satisfied.
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