Abstract
The reinforcement learning algorithms have been proven to be extremely accurate in performing a variety of tasks. These algorithms have outperformed humans in traditional games. This paper proposes a reinforcement learning based approach to autonomous driving. The autonomous vehicles must be able to deal with all external situations to ensure safety and to avoid undesired circumstances such as collisions. Thus, we propose the use of deep deterministic policy gradient (DDPG) algorithm which is able to work in a complex and continuous domain. To avoid physical damage and reduce costs, we choose to use a simulator to test the proposed approach. The CARLA simulator would be used as the environment. To fit the DDPG algorithm to the CARLA environment, our network architecture consists of critic and actor networks. The performance would be evaluated based on rewards generated by the agent while driving in the simulated environment.
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Kadam, R., Vidhani, V., Valecha, B., Bane, A., Giri, N. (2021). Autonomous Vehicle Simulation Using Deep Reinforcement Learning. In: Joshi, A., Khosravy, M., Gupta, N. (eds) Machine Learning for Predictive Analysis. Lecture Notes in Networks and Systems, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-15-7106-0_53
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