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Reinforcement Learning Part 3: Policies, Markov Decision Processes (MDPs), and Trajectories
In the third part of this reinforcement learning (RL) series, we’re going to give a formal definition for a policy and then conceptualize how actions and states play out in a trajectory. While we discussed rewards and returns in the previous post, we’re going to see how Markov Decision Processes (MDPs) provide the underlying foundation…
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Reinforcement Learning Part 2: Rewards, Returns, and the Discount Factor
In this second post on reinforcement learning (RL), we build on the introduction from part 1 by revisiting the idea of a reward and building up to the idea of discounted returns. Recall that the goal of RL is to maximize the rewards earned by the agent over time. We’re going to discuss three main…
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What is Reinforcement Learning?
Reinforcement learning (RL) is a field of study within machine learning (ML) concerned with developing intelligent agents that take actions in dynamic environments in order to maximize their rewards. RL has gained a lot of popularity in the past few years, most notably in robotics, where big-name companies are using it to create robust locomotion…
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When to Use ROS 2 (and When to Skip It): A Guide for Makers and Engineers
Every time I post about ROS 2, I get either “Just use ROS for everything!” or “ROS is bloated overkill.” Both camps have a point, and neither is entirely right. The real answer, as with most engineering decisions, depends entirely on the project in front of you. My Make Magazine article on ROS 2 nodes,…
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How to Get an Embedded Job When AI Is Taking Junior Programming Work
I keep hearing the same concern: AI is taking programming jobs, especially junior positions. Companies want people who can contribute immediately, but AI tools can now write code surprisingly effectively, including drivers, boilerplate templates, test cases, and even help with debugging. In the rest of this post, I want to dig into the stats: are…
