Electrotechnical and Computer Engineering
Vol. 35 No. 01 (2020): Proceedings of the Faculty of Technical Sciences
USING REINFORCEMENT LEARNING TO TRAIN AN AGENT FOR THE CarRacing-v0 ENVIRONMENT
Abstract
This paper presents training and evaluation of the agent for autonomous driving in OpenAI Gym environment CarRacing-v0. Environment is a top-down view of racing track. Agent is trained using reinforcement learning techniques. Algorithms are compared in terms of results achieved in the environment, training time and implementation details. Algorithms that are implemented and evaluated are: Deep Q-Network (DQN), Advantage Actor Critic (A2C) i Asynchronous Advantage Actor Critic (A3C).
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