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John Mosquera mentioned using 'ETFs' and 'HFs type strategies' in the AWS DeepRacer competition. Explain how these trading strategies were translated into machine learning approaches.
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The 'ETFs type strategy' for the qualifying rounds focused on consistent improvement and volatility management, similar to how ETFs aim for stable growth with lower risk. In machine learning terms, this meant creating a reward function that encouraged the DeepRacer to maintain a steady pace, avoid sharp turns, and minimize penalties, ensuring consistent performance across laps. The 'HFs type strategy' for the final round was more aggressive, focusing on maximizing speed and taking calculated risks, like high-frequency trading. This involved a reward function that prioritized speed and efficiency, even if it meant taking more risks, such as tighter turns or higher speeds on straightaways. These strategies demonstrate John's ability to apply domain-specific knowledge to machine learning challenges. John Mosquera translated trading strategies into machine learning by using an 'ETFs type' approach for consistent performance and an 'HFs type' approach for aggressive speed optimization in AWS DeepRacer.