3 ways to get into reinforcement learning

Reinforcement

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

Here’s what I found:

1. Combine work and play with AWS DeepRacer

AWS introduced DeepRacer in November 2018 as the “fastest way to get rolling with machine learning.” In December 2020, they had more than 10,000 competitors and a grand prize that included $10,000 of AWS promotional credits.

Don’t let the competition scare you away, because DeepRacer is a superb learning tool. Your objective is to train the racer to navigate autonomously around a selected racetrack.

When you sign up for DeepRacer, you get access to a simulator where you can select a track, code a reward function, and adjust tuning parameters. There is a default reward function with tuning parameters to start training your racer and evaluating its performance. From there, you’re off to the races to improve your models and tune the algorithms.

You have more than 20 tracks to choose from and can select from simple time trials to head-to-head racing. You can also purchase a physical DeepRacer, load it with your algorithms, and design tracks to run competitive races.

It didn’t take me long to figure out ways to improve the provided reward function. The basic function scores how far the DeepRacer is from the center of the track, with the highest scores when the racer is on the centerline. I improved the algorithm by factoring in the racer’s steering angles, giving it a higher reward when it was steering toward the centerline.

I felt pretty good that with only my second model and 10 minutes of training, my DeepRacer made it around 26% of the track. Of course, my simple model doesn’t work when you factor in obstacles and other racers. You can go it alone to improve your DeepRacer’s performance, or you can learn from others’ code libraries and racing experiences.

2. Be inspired by recent accomplishments

It isn’t difficult to find real-world examples of business, academic, and government organizations experimenting and succeeding with reinforcement learning. Consider these recent headlines:

Several good websites track news in AI and reinforcement learning, including AI Trends, AI News, AI Business, the MIT News page on AI, ScienceDaily’s page on AI, and Berkeley AI Research blog.

3. Experiment with code examples

Before embarking on your reinforcement learning journey, you might want to check out coding examples or books, especially when applied to familiar problems. The following options are worth reviewing:

Lastly, if you’re ready to develop reinforcement learning expertise, consider these courses from Coursera, Harvard, MIT, Stanford, Udacity, Udemy, or review these free options.

Given how hard it is to teach and learn by example, reinforcement learning and other unsupervised learning techniques are areas of growth and opportunity. Even if you are a couple of steps behind in grasping machine learning techniques, understanding reinforcement learning is a chance to develop expertise while academics, industry, and government evolve the science and algorithms.

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Lead Data Scientist