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엄하영(Hayoung Eom),김정환(Jeonghwan Kim),지승윤(Seungyun Ji),최희열(Heeyoul Choi) 한국디지털콘텐츠학회 2020 한국디지털콘텐츠학회논문지 Vol.21 No.2
With the advances in deep learning algorithms, reinforcement learning has shown considerable accomplishments in such tasks as game and physics-based models that require continuous actions. Many platforms and methods like OpenAI Gym were devised to evaluate and compare multiple reinforcement learning algorithms and thus made significant contributions to the deep learning community. In addition to such developments, considering the increasing demand for autonomous vehicles and rule-based parking assistance systems based on attached sensors, we need a parking simulator where reinforcement learning can be applied. In this paper, we develop a new autonomous car parking simulator which allows the learning agent to be trained with reinforcement learning algorithms. The results show the simulator being successfully trained with Deep Deterministic Policy Gradient (DDPG) algorithm.