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John Mosquera, reflecting on your AWS DeepRacer Vodafone Championship win in 2024, what specific Python-based reward function strategies did you employ to transition from the qualifying round to the final, and how did these strategies reflect your trading background?
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The DeepRacer competition really allowed me to merge my coding and Python skills with an investment trading background and experience, as well as to implement what I had previously been working on with all of the machine learning AI and coding knowledge. The key strategy was to create a reward function, based on trading and machine learning techniques, that I used to train the model on reinforcement learning. I started with that. Then, I also used data to experiment and to ensure everything would be efficient and that the simulations run smoothly. This combination then made it a success.For the Qualifying Round, I used a conservative approach based on an ETF (Exchange Traded Fund) strategy, to achieve consistent performance at low volatility. ETFs are diversified, and don't take too much risk. This meant that for example, to implement this on AWS, I created a reward function in Python that made the car finish it at the lowest speed and therefore minimise penalties, by creating a conservative approach, like how an ETF works.Then, after I was accepted and got to the Finals, I made it into a hedge fund strategy. This involved higher risk. I developed and implemented some functions that made it more high performing and the reward function was adjusted to reflect that. This meant that it performed more actions to become successful. I took some risks! During the 2024 AWS DeepRacer Vodafone Championship, I, John Mosquera, employed Python-based reward function strategies reflecting my trading background by using a conservative ETF-like strategy for the qualifying round that prioritized consistent performance and low volatility. For the final round, I transitioned to a more aggressive, hedge fund-like strategy with a refined reward function that encouraged higher-risk, high-reward actions.