This article will delve into my journey, from mastering the prerequisites to competing in this thrilling event, highlighting the significance of the AWS AI/ML scholarship along the way. I’m excited to share my experience in the AWS DeepRacer Student League.
AWS DeepRacer: A Glimpse of the Future:
AWS DeepRacer is a groundbreaking initiative that combines
the thrill of racing with the power of reinforcement learning (RL) and
autonomous driving. Participants train RL models to navigate a 3D simulated
racetrack using AWS services. The competition serves as an engaging platform to
learn and apply machine learning concepts practically.
The AWS AI/ML Scholarship: Unlocking Opportunities:
To qualify for the AWS DeepRacer Student League, I first
completed a foundational machine learning course, equipping me with essential
knowledge. Next, I demonstrated my passion and commitment by scoring over 24
points out of 30 in a challenging multiple-choice question (MCQ) test. Finally,
I demonstrated the art of racing by completing three laps in under three minutes,
showcasing my ability to apply machine learning concepts effectively.
But that’s not all. The machine learning algorithm that I
created didn’t just meet the three-minute requirement; it also ranked among the
top 100 fastest laps around the track. This emphasizes how easily the
prerequisites can be fulfilled by implementing a set of effective strategies
outlined in the upcoming section.
Improving your own ML Algorithms with AWS DeepRacer
parameters:
To succeed in the AWS DeepRacer Student League, it’s
essential to develop and fine-tune your machine learning algorithms. AWS
DeepRacer provides a unique environment for reinforcement learning, and
optimizing your model can be a rewarding experience. You can view some of the
default Input parameters for DeepRacer at AWS
Docs. Here are some tips to help you get started:
- Hyperparameter
Tuning: Experiment with various hyperparameters such as
learning rate, exploration factor, and discount factor to find the optimal
balance between exploration and exploitation.
- Reward
Function: Craft a well-defined reward function that
incentivizes your model to follow the track and make informed decisions.
This is a crucial aspect of reinforcement learning.
- Simulation: Use
AWS DeepRacer’s built-in simulation environment to train and evaluate your
model efficiently before deploying it to the virtual track.
- Data
Collection: Gather ample data by running your model in the
simulator, as more data can lead to better training results.
- Iterate
and Experiment: Don’t be afraid to iterate on your model and
experiment with different techniques. Reinforcement learning is an
evolving field, and continuous learning is key.
Here's a sample code for following the centerline algorithm
which divides the track into three reward zones. The farther the car strays
from the centerline, the less it’s rewarded.
def reward_function(params):
# Example of
rewarding the agent to follow center line
# Read input
parameters
track_width =
params['track_width']
distance_from_center
= params['distance_from_center']
# Calculate 3
markers that are at varying distances away from the center line
marker_1 = 0.1 *
track_width
marker_2 = 0.25 *
track_width
marker_3 = 0.5 *
track_width
# Give higher
reward if the car is closer to center line and vice versa
if
distance_from_center <= marker_1:
reward = 1.0
elif
distance_from_center <= marker_2:
reward = 0.5
elif
distance_from_center <= marker_3:
reward = 0.1
else:
reward = 1e-3
# likely crashed/ close to off track
return
float(reward)
Key Takeaways: Embracing the DeepRacer Experience
Participating in the AWS DeepRacer Student League has been a
transformative journey, offering valuable lessons about the world of machine
learning and life itself. Here are some key takeaways that not only apply to
DeepRacer but are also universally welcoming:
- Starting
from the Simple and the Slow: DeepRacer taught me the importance
of starting with the basics. Just like in life, beginning with simple,
well-understood concepts often yields the best results. It's a welcoming
reminder that a solid foundation is key to any endeavor.
- Small
Steps Lead to Big Changes: DeepRacer demonstrates that small,
incremental progress can lead to significant improvements. This applies to
both racing laps and personal growth. By consistently taking small steps
forward, we can achieve remarkable transformations over time.
- Believe
in Miracles, Persevere, and Achieve: While it might take a
million tries, believing in the possibility of miracles and persevering
through challenges can lead to extraordinary accomplishments. DeepRacer
encourages us to keep pushing our limits and believing in our potential.
- Learning
from the Good and the Bad: In machine learning, as in life, it's
crucial to recognize that anything capable of learning will absorb both
good and bad habits. This welcoming insight reminds us to be mindful of
the influences around us and strive for positive growth.
In conclusion, the AWS DeepRacer Student League is not just
a thrilling competition but also a welcoming educational journey that imparts
valuable life lessons. These takeaways are not limited to the racetrack; they
are principles that can guide us toward success and fulfillment in all aspects
of life. So, whether you're interested in machine learning or simply seeking
personal growth, the welcoming lessons of DeepRacer can be a beacon of
inspiration and enlightenment.