Car Simulation Using Reinforcement Learning
نویسنده
چکیده
This project report presents the result of Reinforcement Learning (RL) experiments in a car simulation. W ithout any knowledge of the tracks in advance, the car can be trained to avoid bumping into the walls by learning from the given rewards. We have built a car simulation system in which the car can be trained and tested on the tracks with several RL algorithms , including Actor-Critic method, SARSA(0) and SARSA(λ). We have also compared the results and given some ideas of future work.
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