To solve the problem in which, although autonomous driving is dealt with reinforcement learning algorithms based on the Q-learning or policy gradient, rule-based methods cannot change scale well whereas reinforcement learning methods based on the Q-learning or policy gradient are not sample-efficient and are very time consuming to solve because of the need to explore a large state space.SOLUTION: A framework combines vision and sample-efficient reinforcement-learning on the basis of guided policy search for autonomous driving. A controller extracts environmental information from vision and is trained to drive using reinforcement learning.SELECTED DRAWING: Figure 1
VISION-BASED SAMPLE-EFFICIENT REINFORCEMENT LEARNING FRAMEWORK FOR AUTONOMOUS DRIVING