… [8] optimized the fuel efficiency of hybrid cars by implementing … machine learning, reinforcement
learning, partially observable Markov decision processes, inverse reinforcement learning…

We consider the problem of reward learning for temporally extended tasks. For reward
learning, inverse reinforcement learning (IRL) is a widely used paradigm. Given a Markov …

… -making problem using the Markov Decision Process framework. We propose an Inverse
Reinforcement Learning-based method, based on which synthetic population vehicle …

… [14, 15] proposed an inverse reinforcement learning algorithm based on maximum marginal
… for inverse reinforcement learning mainly choose the characteristics of the main vehicle …

… Planning and reinforcement learning algorithms for automated driving often solve a
Markov-Decision Process (MDP) to find an optimal action sequence. The actions in automated …

… -vehicle conflicts are modeled using single-agent and multi-agent approaches under the
Markov Decision … A continuous Gaussian Process Inverse Reinforcement Learning (GPIRL) …

… This robotic car was a milestone in the quest for self-driving cars. The pervasive use of …
Inverse reinforcement learning is formulated within the framework of Markov decision processes (…

… for automated vehicles is limited by the difficulty of designing reward functions. Most existing
… inverse reinforcement learning method, Approximate Variational Reward Learning (AVRL), …

… achieves more accurate vehicle trajectory prediction by considering vehicle behavioral intent
… method, which has excellent performance in modeling continuous decision sequences, to …

… Safety-critical control and decision-making … vehicle (UAV) crashing due to unexpectedly
large wind gusts or an autonomous car failing to accommodate for an erratic neighboring vehicle)…