Reinforcement Learning Vs. Deep Reinforcement Learning: What’s the Difference?
Machine learning algorithms can make life and work easier, freeing us from redundant tasks while working faster – and smarter – than entire teams of people. However, there are different types of machine learning. For example, there’s reinforcement learning and deep reinforcement learning.
“Even though reinforcement learning and deep reinforcement learning are both machine learning techniques which learn autonomously, there are some differences,” according to Dr. Kiho Lim, an assistant professor of computer scienceat William Patterson University in Wayne, New Jersey. “Reinforcement learning is dynamically learning with a trial and error method to maximize the outcome, while deep reinforcement learning is learning from existing knowledge and applying it to a new data set.”
But what, exactly, does that mean? We went to the experts – and asked them to provide plenty of examples!
As Lim says, reinforcement learning is the practice of learning by trial and error – and practice. “In this discipline, a model learns in deployment by incrementally being rewarded for a correct prediction and penalized for incorrect predictions,” according to Hunaid Hameed, a data scientist trainee at Data Science Dojo in Redmond, WA. (Read Reinforcement Learning Can Give a Nice Dynamic Spin to Marketing.)
“Reinforcement learning is commonly seen in AI playing games and improving in playing the game over time.”
The three essential components in reinforcement learning are an agent, action, and reward. “Reinforcement learning adheres to a specific methodology and determines the best means to obtain the best result,” according to Dr. Ankur Taly, head of data science at Fiddler Labs in Mountain View, CA. “It’s very similar to the structure of how we play a video game, in which the character (agent) engages in a series of trials (actions) to obtain the highest score (reward).”
However, it’s an autonomous self-teaching system. Using the video game example, Taly says that positive rewards may come from increasing the score or points, and negative rewards may result from running into obstacles or making unfavorable moves.
Chris Nicholson, CEO of San Francisco, CA-based Skymind builds of the example of how algorithms learn by trial and error.” Imagine playing Super Mario Brothers for the first time, and trying to find out how to win: you explore the space, you duck, jump, hit a coin, land on a turtle, and then you see what happens.”
By learning the good actions and the bad actions, the game teaches you how to behave. “Reinforcement learning does that in any situation: video games, board games, simulations of real-world use cases.” In fact, Nicholson says his company uses reinforcement learning and simulations to help companies figure out the best decision path through a complex situation.
In reinforcement learning, an agent makes several smaller decisions to achieve a larger goal. Yet another example is teaching a robot to walk. “Instead of hard-coding directions to lift one foot, bend the knee, put it down, and so on, a reinforcement learning approach might have the robot experiment with different sequences of movements and find out which combinations are the most successful at making it move forward,” says Stephen Bailey, data scientist and analytics tool expert at Immuta in College Park, MD.
Aside from video games and robotics, there are other examples that can help explain how reinforcement learning works. Brandon Haynie, chief data scientist at Babel Street in Washington, DC, compares it to a human learning to ride a bicycle. “If you’re stationary and lift your feet without pedaling, a fall – or penalty – is imminent.”
However, if you start to pedal, then you will remain on the bike – reward – and progress to the next state.
“Reinforcement learning has applications spanning several sectors, including financial decisions, chemistry, manufacturing, and of course, robotics,” Haynie says.
