… vehicle and the environment as a stochastic Markov decision … behavior of the autonomous
vehicle is obtained as follows: … autonomous vehicle using reinforcement learning techniques. …

… This paper uses the inverse reinforcement learning … vehicles, when passing through an
intersection. The rest of this paper is organized as follows: Section 2 introduces the decision-…

… decisions, we propose a hierarchical inverse reinforcement learning (IRL) framework in this
paper, to learn … strongly depends on the future plans of the host vehicle. To address such …

… vehicles when navigating in close proximity to other vehicles, pedestrians, and cyclists. Thanks
to the recent advances in deep learning and inverse reinforcement learning (IRL… decision …

… to make decisions by imitating human demonstration actions [… of learning decision-making
policy directly, we learn the … the surrounding vehicles in response to the ego vehicle’s actions. …

… learn the driver’s car-following preferences from historical data using model-based maximum
entropy Inverse Reinforcement Learning (… learning for autonomous vehicle decisionmaking,…

… proposed method is to tell the vehicle at what time and to which vehicle gap it should make
the … of the decision-making task, different driving styles show different preferences of vehicle …

… In recent years, reinforcement learning shows the potential in solving sequential decision …
each driver data based on the inverse reinforcement learning algorithm, and r visualization is …

… be beneficial for making decisions to improve services. Imitation of multi-vehicle movement
on … to analyze vehicle routing to passengers and traffic congestion. In addition, the number of …

… The car-following process is essentially a sequential decision-making … ’s car, the spacing
between the driver’s car and the leading car, and the relative speed between the two vehicles. …