Comparative Study of NeuroEvolution Algorithms in Reinforcement Learning for Self-Driving Cars

Abstract

Neuroevolution has been used to train neural networks for challenging deep Reinforcement Learning (RL) problems like Atari, image hard maze, and humanoid locomotion. The performance is comparable to the performance of neural networks trained by algorithms like Q-learning and policy gradients. This work conducts a detailed comparative study of using neuroevolution algorithms in solving the self-driving car problem. Different neuroevolution algorithms are used to train deep neural networks to predict the steering angle of a car in a simulated environment. Neuroevolution algorithms are compared to the Double Deep Q-Learning (DDQN) algorithm. Based on the experimental results, the neuroevolution algorithms show better performance than DDQN algorithm. The Evolutionary Strategies (ES) algorithm outperforms the rest in accuracy in driving in the middle of the lane, with the best average result of 97.13%. Moreover, the Random Search (RS) algorithm outperforms the rest in terms of driving the longest while keeping close to the middle of the lane, with the best average result of 403.54m. These results confirm that the entire family of genetic and evolutionary algorithms with all their performance optimization techniques, are available to train and develop self driving cars.