Reinforcement Learning in Data Science
As a subset of artificial intelligence (AI), machine learning has its own subsets, including deep learning. While machine learning is a type of AI that produces algorithms and modifies itself without human intervention, deep learning is a category of machine learning that works with a network of algorithms, called an artificial neural network. Each algorithm in a neural network provides its own interpretation of the data available. The result is a functioning system that is, in many ways, similar to the human brain.
Reinforcement learning is itself a subset of deep learning. It’s often compared to other subgroups such as supervised and unsupervised learning. Reinforcement learning will likely play a significant role in the future of AI. Below, you’ll learn more about what it is, how it works, and what its potential impact could be.
Trends in Machine Learning: What Is Reinforcement Learning?
Reinforcement learning allows an agent to learn by doing. It’s often described as learning by trial and error or by penalties and rewards. In a reinforcement learning environment, an agent or reinforcement learning algorithm tries to solve a problem. As it works to figure out an issue, the agent gets rewards when it does something correctly or penalties when it makes a mistake. Ultimately, it wants to maximize the rewards it receives while minimizing or completely eliminating the penalties.
Reinforcement learning differs from other forms of deep learning because the agent isn’t given any specific instructions or details. While in other learning environments, an agent might be given a prompt such as, “If ___, then ___,” no prompts are given to an agent in reinforcement learning. You might think of it as the AI equivalent of throwing someone into the water and hoping they learn to swim. Another way to look at reinforcement learning is through the lens of popular games.
For example, in the classic arcade game Pacman, a player wants to eat as many dots or rewards as they can while avoiding contact with ghosts, which are penalties. The agent — in this case, Pacman — exists in an interactive environment, which is the game’s maze. It needs to move through the maze in a way that allows it to get the greatest number of rewards while minimizing or avoiding penalties.
As it moves through the game, the Pacman or agent needs to learn how to trade off between rewards and penalties. It might need to sacrifice some rewards to dramatically reduce the risk of contact with a penalty. Eventually, reinforcement learning will allow the agent to get to the point where it wins the game most of the time.
Another example of reinforcement learning can be seen in chatbots that learn over time. Chatbots that provide customer service need to guide a client toward a solution to their problem. The solution might be putting that person in touch with a live agent or walking the client step-by-step through a troubleshooting process.
For the chatbot to learn the skills necessary to provide the best service possible, it can be presented with a series of rewards when it offers the correct suggestion to a client. Similarly, it can be given penalties when it recommends something that doesn’t help a client. Reinforcement learning and optimal control can work hand-in-hand to make the overall process more intuitive and efficient.
Reinforcement Learning for Data Science
Reinforcement learning can and should be among the tools data scientists use. The goal of reinforcement learning is to maximize the reward to the agent by finding an appropriate action model. The typical reinforcement learning model looks like a loop, with an agent performing an action in an environment that alters its state or leads to a reward.
Often, mathematical frameworks called Markov Decision Processes (MDPs) describe the environment used in reinforcement learning. Data scientists can use MDPs to formulate virtually every reinforcement learning problem. To create an MDP, a data scientist should define a set of environment states, a set of actions, possible reward functions, and potential transitions or maps. Although MDPs have their uses and benefits, they can be challenging to incorporate into real-world environments, where there isn’t existing knowledge of the setting’s dynamics.
In those situations, a model-free method might best serve data science. Q-learning is an example of a model-free reinforcement learning process. Q-learning is a type of reinforcement learning that uses Python. With Q-learning, the value of the performing action is updated based on what an agent learns from a particular value.
Another example of a model-free process is State-action-reward-state-action (SARSA). During SARSA, the agent learns value from a current action that stems from a current policy. Although the two methods are easy to put into practice, they do have some drawbacks. Notably, neither can estimate values when the state is unknown.
As you might guess, reinforcement learning is a data-heavy process. It works best in situations where a lot of data are readily available, such as in robotics or gaming. A well-known example of successful reinforcement learning is AlphaGo, which was developed by Google. AlphaGo AI defeated the reigning human Go player after two games in a three-game series.
What Is the Impact of Reinforcement Learning?
Beyond defeating Go champions, what is the potential for reinforcement learning? The machine learning subset has several potential applications.
One of those applications is in the use and development of self-driving cars. Self-driving cars need to learn what the rules of the road are, how to adjust to those rules, how to avoid collisions, and how to adapt to sudden changes on the road. Sudden changes may include things like an animal, small child, or ball suddenly appearing on the street.
Q-learning can help autonomous cars learn how to change lanes while maintaining speed and avoiding contact with other vehicles on the road. Autonomous cars can also learn how to parallel park properly through trial and error and by learning from established parking rules.
Robotics is another promising application for reinforcement learning. Google’s DeepMind has been used for more things than just winning Go games. The robots have also been developed to keep the company’s data centers cool. The autonomous cooling system helped Google significantly reduce its energy consumption and energy costs.
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