Evaluation of Reinforcement Learning Algorithms Applied to an Autonomous Car Model for Educational Purposes
摘要
Currently, autonomous cars are extensively studied by many institutions and companies with the aim of practical implementation. Q-learning is an algorithm of reinforcement learning, which does not require a model and can be seen as an asynchronous method of dynamic programming. It allows agents to learn to take action optimally in a Markovian environment by experiencing the outcomes of actions without the need to build a probability model. This paper presents the process of developing an autonomous vehicle model for educational purposes that can self-optimize its decision-making options based on a reinforcement learning algorithm such as Q-learning. The algorithm helps train autonomous cars to avoid obstacles collisions. The preliminary results show that the Q-learning algorithm is successful in building a self-training technique to adapt to specific requirements.