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dc.contributor.authorHO, KUOK HOU(何國豪)-
dc.identifier.citationHo, K. H. (2021). A Study of Deep Q Network, Soft Actor Critic Algorithm in CARLA (OAPS)). Retrieved from University of Macau, Outstanding Academic Papers by Students Repository.en_US
dc.description.abstractThis project is primarily focused on the advancement of machine learning-based automated driving systems. The majority of our time in this mission will be spent studying reinforcement learning (RL) algorithms. We chose to learn because the special role of reinforcement learning gives it an advantage in the creation of autonomous driving models. We must determine which algorithm to use in our project since RL belongs to several algorithms. We tested several methods during this year, like DQN, SAC, etc. We finally decided to use Proximal Policy Optimization (PPO) to develop the self-driving model after becoming acquainted with RL and conducting research. The platform that we need to build and test the self-driving model in is a simulator named CARLA (Car Learning to Act). CARLA is an open-source simulator built with Unreal Engine 4 by Intel Visual Computing Lab for autonomous driving cars testing. For training, the simulation platform provides free models, such as vehicle models and maps. The client, which is written in Python, will enable the autonomous driving system to communicate with the environment in the CARLA server. CARLA server is capable of simulating real-world elements such as lights, weather, and complex actors.en_US
dc.titleA Study of Deep Q Network, Soft Actor Critic Algorithm in CARLAen_US
dc.contributor.departmentDepartment of Computer and Information Scienceen_US
dc.description.instructorProf. Leong Hou Uen_US
dc.contributor.facultyFaculty of Science and Technologyen_US
dc.description.courseBachelor of Science in Computer Scienceen_US
dc.description.programmeBachelor of Science in Computer Scienceen_US
Appears in Collections:FST OAPS 2021

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