Recent advances in combining deep learning and Reinforcement Learning have shown a promising path for designing new control agents that can learn optimal policies for challenging control tasks. These new methods address the main limitations of conventional Reinforcement Learning methods such as customized feature engineering and small action state space dimension requirements. In this paper, we leverage one of the state-of-the-art Reinforcement Learning methods, known as Trust Region Policy Optimization, to tackle intersection management for autonomous vehicles. We show that using this method, we can perform fine-grained acceleration control of autonomous vehicles in a grid street plan to achieve a global design objective.
A decentralized optimal control formulation is proposed in @cite_1 in which the acceleration deceleration of the vehicles are minimized subject to collision avoidance constraints.
@cite_1
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In earlier work, we addressed the problem of coordinating online an increasing number of connected and automated vehicles (CAVs) crossing two adjacent intersections in an urban area. The analytical solution, however, did not consider the state and control constraints. In this paper, we present the complete Hamiltonian analysis including state and control constraints. In addition, we present conditions that do not allow the rear-end collision avoidance constraint to become active at any time inside the control zone. The complete analytical solution, when it exists, allows the vehicles to cross the intersection without the use of traffic lights and under the hard constraint of collision avoidance. The effectiveness of the proposed solution is validated through simulation in a single intersection and it is shown that coordination of CAVs can reduce significantly both fuel consumption and travel time.