Reinforcement learning

Reinforcement learning is a method of machine learning in which a learning agent, or the entity undergoing training, learns through interactions with its environment. These interactions are usually guided to some degree by a feedback system with built-in rewards and punishments automatically doled out in response to the agent’s actions. In short, reinforcement learning is essentially a trial-and-error process that ultimately allows the agent to gather the information it needs to achieve the best result. Reinforcement learning is particularly important and useful because, unlike other forms of machine learning, it empowers an agent to successfully navigate the intricacies of the environment in which it operates. Although it has some distinct advantages, reinforcement learning also comes with some key challenges, such as issues that arise from frequent environmental changes that have thus far limited its real-world deployment and use. Nevertheless, it has many potential practical applications, especially in gaming and robotics.

Background

Reinforcement learning is one of the three basic paradigms of machine learning, which is a specific branch of artificial intelligence (AI) and computer science. It is focused on using data and algorithms to imitate human learning and gradually improve accuracy. In addition to reinforcement learning, the other basic paradigms of machine learning include supervised and unsupervised learning. While all three share some similarities, they are also all unique.

Supervised learning is a type of machine learning that relies on labeled datasets. Developers use these datasets to program algorithms to carry out tasks like accurately predicting outcomes or classifying data. With the aid of labeled datasets, an algorithm can carefully monitor its accuracy and gradually learn. This “learning” arises as the algorithm makes predictions about data and adjusts for the correct answer.

Unsupervised learning is a form of machine learning in which an algorithm is used to analyze and cluster various unlabeled data. The algorithms used in unsupervised learning can discover hidden patterns in data without any intervention on the part of human operators. Unlike supervised learning, unsupervised learning does not rely on the use of labeled datasets. In other words, algorithms with unsupervised learning models do the work on their own instead of being guided by a developer.

Reinforcement learning bears some similarities to both supervised and unsupervised learning. Like unsupervised learning, reinforcement does not require the use of specifically labeled data. On the other hand, somewhat akin to supervised learning, it involves the use of a preprogrammed feedback system of rewards and punishments that allows an agent to determine the best course of action as it navigates its environment. As a result of all this, reinforcement learning can to some degree be regarded as a machine learning paradigm that bridges the gap between supervised and unsupervised learning. At the same time, it also serves as a unique alternative to the other two common paradigms offering another potential approach to machine learning that could be utilized for various distinct purposes.

Overview

Reinforcement learning is a machine learning paradigm in which developers create and implement a feedback system that includes rewards for desired behaviors and punishments for undesired behaviors. As part of this method, developers specifically assign positive values to actions they want to encourage the agent to use and negative values to actions they want the agent to avoid. Armed with this basic information, the agent is empowered to seek out both long-term and maximum possible rewards as the optimal solution to a given challenge. As it explores its environment over time, the agent learns to seek positive actions and avoid negative actions.

The underlying principle of reinforcement learning is the Markov decision process, which was named after and based on the work of late nineteenth and early twentieth century Russian mathematician Andrey Markov. In the Markov decision process, an agent exists in a specific state within an environment and is required to choose the best possible option among the different actions that it can potentially perform in its given state. Additional potential actions are offered as a reward for the correct choice. Should it make the correct choice, the rewarded actions are made available to the agent when it enters its next state. As this process plays out over time, the agent’s cumulative reward is the sum of all the rewards it receives based on the positive actions it chooses along the way.

Reinforcement learning has certain advantages and disadvantages as a machine learning paradigm. Perhaps the most important advantage of reinforcement learning is that it makes it easier for agents to focus on big picture problems. Unlike other approaches to machine learning that are primarily aimed at ensuring the successful execution of specific subtasks, reinforcement learning is designed to directly maximize long-term benefits. Reinforcement learning is also advantageous because training data comes from the agent’s experience and therefore does not need to be separately fed to the algorithm. A final advantage of reinforcement learning works particularly well in dynamic, changing environments. On the other side of the coin, reinforcement learning can be disadvantageous in that it requires a great deal of agent experience and may make it more difficult and time-consuming for agents to reach the optimal solution because it prioritizes long-term goals over short-term goals. Another challenge with reinforcement learning is it may be difficult for observers to accurately interpret an agent’s actions since the agent acts independently based on its own experience.

Although there is a great deal of interest surrounding reinforcement learning in the modern tech community, real-world applications of the paradigm remain limited. There have been some successful use cases, however. One of the most common uses of reinforcement learning is in gaming. Agents relying on reinforcement learning have been able to achieve superhuman performance in video games like Pac-Man. Reinforcement learning is also useful in robotics, particularly for robots that operate in uncertain environments where it would be impossible to pre-program accurate actions. Reinforcement learning is a valuable tool to accomplish certain tasks involved in autonomous driving. It is particularly useful for tasks like vehicle path planning and motion prediction.

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