Q&A: What Is Reinforcement Learning?

By Indeed Editorial Team

Published July 13, 2021

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Artificial intelligence (AI) and machine learning engineers often rely on reinforcement learning when implementing new AI programs and applications. Developing your understanding of machine learning and its methods can help you build your skills and industry knowledge. If you're considering a career in machine learning and software engineering for AI systems, it can be beneficial to understand the different subfields. In this article, we provide an overview of some of the common questions about reinforcement learning to provide more insight into this career field in technology.

What is reinforcement learning?

Reinforcement learning is a subfield of machine learning and AI processes that focuses on learning techniques that train an agent to learn in an environment that presents trial-and-error interactions. The agent uses feedback it collects from its own performance and experiences to form information that reinforces its future actions when performing similar functions. Similar to the methods of deep learning, supervised learning and unsupervised learning, this method of machine learning aims to support the independent and intelligent function of artificial intelligence systems.

Related: What Is Artificial Intelligence?

Why is reinforcement learning important?

Reinforcement learning is critical to processes in machine learning and artificial intelligence applications. Computer and software engineers rely on this type of machine learning to establish parameters and operational standards for soft AI to follow when retrieving and displaying information, such as a search assistant on a mobile device. Several more reasons this subfield of AI is advantageous include:

  • Establishes standards of procedure for digital and technical systems to follow

  • Creates interactive environments for computerized agents to build frameworks for future actions

  • Reinforces programming and computer code that artificial intelligence applications like robotics rely on to function

Related: Everything You Need To Know About the Role of an AI Engineer

What are the components of reinforcement learning?

Within machine learning that applies reinforcement parameters, you have an agent and the environment in which the agent performs. Besides these two components, however, there are several more elements that can be essential to a reinforcement learning system:

  • Policies: This field of machine learning uses policies to define an agent's behavior during a specific period. The policy engineers implement essentially maps the state of the environment to the action and the action to the agent's behavior within the environment.

  • Rewards: Rewards establish goals for reinforcement learning problems, where the agent receives a reward signal for completed desired outcomes.

  • Value functions: The value functions in a system represent the total number of rewards the agent can expect in the future if it initiates actions in its current environmental state.

  • Environment model: Some systems use models of the environment to reproduce behaviors specific to the environment, giving engineers a way to make inferences about how environments may react to agents.

Related: What Is Deep Learning?

What processes does reinforcement learning follow?

Data input into an agent travels through the environment to perform a set of actions. If the actions are correct, programmers reward the agent by reinforcing what actions the agent took to achieve the outcome. If the actions are incorrect, programmers punish the agent for performing the wrong actions. The "punishment," in this case, is a reconfiguration of sophisticated software code that establishes parameters for recognition in the agent that supports it when identifying incorrect actions before performing them. These steps reinforce the agent to keep performing the correct processes to achieve the desired outcome.

What are the types of reinforcement learning?

In reinforcement learning, engineers can apply either positive or negative learning methods to train agents and environments to perform desirable actions. Positive reinforcement occurs when agents take a specific set of actions or perform a certain behavior. This method helps increase the strength and frequency of the desired behavior that an agent exhibits. Positive reinforcement also impacts the agent by confirming the validity of its actions, thus increasing the likelihood of the agent repeating the behavior.

Negative reinforcement, in comparison, strengthens an undesirable action or behavior due to negative conditions that an agent should otherwise avoid. While positive reinforcement can help you maximize the performance of states, negative reinforcement tells agents and environments what the minimum standard of performance is, resulting in enough functionality to meet the minimum behavioral standards engineers set for the system.

Related: What Are the Types of Machine Learning? (Plus When To Use Them)

What are the differences between reinforcement and supervised learning?

Reinforcement and supervised learning are both subfields of machine learning that rely on processes of deep learning to interpret input data and produce successful outputs. Although the two disciplines share similarities, there are several differences in the way engineers and programmers complete processes within the environments. Unlike supervised learning, in reinforcement learning, the interaction between agents and environments occurs in discrete steps to complete either exploitation or exploration tasks. This results in the distinct pathway for agents to follow to achieve results, where:

  • The system contains an agent, a model of the neural network and an environment.

  • The parameters use the elements of value, action, reward and next-state procedures to set policies that train the neural network model.

  • The policy to trains the agent to perform the specific actions to maximize cumulative rewards from the actual environment.

Unlike reinforcement learning, supervised learning performs either regression or classification tasks to analyze and establish training data. The training data then establishes parameters between an agent's and environment's actions to produce generalized outputs. This achieves distinct pairs of input and output values, where a supervised learning environment uses various algorithms to perform specific actions. So instead of using decision-making processes and mathematical frameworks for modeling, supervised learning processes require:

  • A dataset with labels and object annotations to each value of the dataset

  • Training parameters from the dataset to guide neural networks in mapping data to respective labels

  • Performance evaluations to evaluate the trained model's efficiency, functionality and ability to achieve desired results

Related: Machine Learning vs. Deep Learning: What's the Difference?

What are some drawbacks of reinforcement learning?

Although reinforcement learning is advantageous for various applications that establish independent AI systems, there can be several challenges that engineers and programmers sometimes solve when working with this subfield of machine learning:

  • State overload: In cases of positive reinforcement learning, too much reinforcement can result in state overload, which is when the environmental state becomes too full of input information that diminishes output results.

  • Heavy data reliance: This field of machine learning is often more suitable for complex problems rather than for solving simple problems, therefore requiring large amounts of data for agents and environments to perform.

  • Limited modeling: Because this field of machine learning uses the Markov model of reinforcement training, it can sometimes lead to limitations in computations of probability, sequential reasoning and event modeling.

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