… 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 …

… learning-based planner to drive a car in dense, urban traffic using Inverse Reinforcement
Learning (… However, the critical motion planning and decision-making algorithms that ultimately …

… 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. …

… to deal with these MDP decision problems by using inverse reinforcement learning to find
the unknown reward function. Inverse reinforcement learning can recover the unknow reward …

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

… Hence, the state space in our study only includes features from relevant vehicles, ie
vehicles V0,V1,V2,V3,V4 forming gap 0 to gap 3, as shown in Fig. 3. In this study, we refer to …

… The way that these functions are weighted is usually unknown even to the decision maker. …
of a series of complex activities (called sequential decisions in our work). A novel algorithm …