Reinforcement Learning for Platooning Control in Vehicular Networks.

Truck platooning is a promising technology that can reduce costs (fuel consumption) and enhance the overall transportation productivity. While recent research has focused on platoons' network and stability, few studies have tackled platooning formation and control. This paper uses Reinforcement Learning (RL) to study the dispatching control of trucks with arriving platoons, a problem first proposed in [1]. This work builds on [1] by considering the lack of the cost function and statistical knowledge. In particular, we employ Q-learning to compute the optimal dispatch control policy at a highway hub. Given the unbounded state space of the model, traditional Q-learning may converge slowly or even get stuck in sub-optimal policies. We improve Q-learning by confining the agent to transition in a finite subset of the state space. For this purpose, we use the switching condition property of the optimal policy (derived in [1]), the underlying random walk model, and a sensitivity analysis of the cost function. Our numerical results demonstrate that our Enhanced Q-learning converges significantly faster (up to 97%) in terms of CPU time and number of interactions.