NeuroAction: a neuroevolutionary approach to reinforcement learning for autonomous vehicles

End-to-end or Deep Reinforcement Learning-based control of autonomous vehicles generally leverages a sequence of decoupled perception-action protocols. One main limitation of such frameworks is the required backpropagation algorithm to optimize the underlying mapping function or policy network. This is because although the learning goal usually involves several objectives, they must be aggregated to realize a single objective loss utilized by the backpropagation algorithm. This also limits the preference-based driving behavior from a user perspective. To overcome these challenges, we present NeuroAction—a multi-objective neuroevolutionary method designed for reinforcement learning-based autonomous driving where several goals or objectives can be optimized simultaneously. Specifically, we propose a formulation of reinforcement learning-based control of autonomous vehicles as a multiobjective optimization problem. Consequently, any multiobjective evolutionary algorithm can be used to solve the resulting problem with the aim of generating a Pareto-front of optimal policy networks. In other words, the resulting framework is capable of generating policies that are suitable for providing users with different trade-offs based on their desired driving preferences. We investigated the proposed framework on a benchmark DRL-based autonomous driving task and presented performance evolution based on three different EMO algorithms.