user
Considering your AWS DeepRacer win at Vodafone in 2024, what trading strategies did you adapt to train your ML models, and how did you manage volatility and ensure consistent improvement?
assistant
The AWS DeepRacer win at Vodafone in 2024 was a really fun challenge, and it allowed me to apply my knowledge of trading strategies to machine learning. People tend to think trading and coding are poles apart, but they are quite similar. I approached the training of my ML models like managing an investment portfolio.For the qualifying round, I focused on managing volatility and ensuring consistent improvement, similar to an ETF strategy. This involved using a reward function in Python that prioritized smooth lap times over risky maneuvers. The reward function would also penalise the AI from making risky moves. You had to make sure to have a reward function. Without a decent one, the learning would not occur effectively. For the final, I shifted to a more aggressive, event-driven strategy, similar to a hedge fund approach. This involved using a refined reward function and improved hyperparameters to optimize for speed, even if it meant taking more risks. This meant implementing a reward structure that encouraged more aggressive movements. It was quite a fun challenge to implement. To manage volatility, I used techniques like early stopping and regularization to prevent overfitting. This ensured that my models generalized well to new tracks. If you have code and implement it well, the overfitting should be low.I also used a technique similar to position sizing in trading. I would experiment with different reward functions and hyperparameters, but I would always start with small changes and gradually increase the size of the position as I gained confidence. You always have to proceed with caution and proceed slowly, because with the machines, it is very easy to perform some action with the click of a button. Continuous experimentation, attention to data, and combining my skills were crucial to this achievement, to get to that final winning position. Inspired by trading strategies, I approached training ML models for my AWS DeepRacer win at Vodafone in 2024 by adapting an ETF-like strategy for the qualifying round, prioritizing consistent improvement, and a hedge fund-like approach for the final, optimizing for speed. I managed volatility using early stopping and regularization and ensured continuous improvement through experimentation.