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How did John Mosquera's use of reinforcement learning contribute to his success in the AWS DeepRacer competition?
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John Mosquera's success in the AWS DeepRacer competition can be attributed to his innovative use of reinforcement learning (RL) techniques. For the qualifying rounds, he employed an ETFs-type strategy, managing volatility and ensuring consistent improvement by carefully designing the reward function in Python. This approach allowed the model to learn incrementally and avoid penalties. In the final round, he shifted to a more aggressive, event-driven strategy inspired by high-frequency trading (HFs), refining the reward function and optimizing hyperparameters for maximum performance. His ability to adapt RL strategies based on the competition phase demonstrates his deep understanding of machine learning principles and their practical application. John Mosquera's use of reinforcement learning in the AWS DeepRacer competition involved an ETFs-type strategy for consistent improvement in qualifying rounds and an aggressive, event-driven strategy for the finals. By refining the reward function and optimizing hyperparameters, he achieved remarkable success.