What Is Reinforcement Learning in AI and How Does it Work?

By Indeed Editorial Team

Published May 31, 2022

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.

Machine learning engineers who program artificial intelligence (AI) often rely on reinforcement learning when they implement new AI applications and programs. By developing their knowledge of machine learning and the methods it uses, these engineers can develop their skills and industry knowledge. Understanding the various subfields of machine learning and how they work can be highly beneficial if you wish to pursue a career in intelligent programming and software engineering for AI systems.

In this article, we define reinforcement learning, review its benefits, explore the components and types of learning by reinforcement, identify the processes that it follows, examine the drawbacks and applications of the process, and describe the difference between reinforcement and supervised learning.

What is reinforcement learning?

Reinforcement learning is a subfield of machine learning and artificial intelligence processes which establish learning techniques to train agents in a trial-and-error environment. In AI, an agent is anything which can perceive its environment, take autonomous action, and learn from trial-based processes. Agents use feedback gained from their own performance to reinforce patterns for future behaviour in this process of learning through reinforcement. As with deep learning, supervised learning, and unsupervised learning, reinforcement machine learning strives to support an AI system's intelligent and independent functioning.

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Benefits of learning by reinforcement models

AI and machine learning applications can rely heavily on learning with reinforcement. Software and computer engineers frequently use reinforcement machine learning to create operational standards and parameters for soft AI to follow when fetching and displaying information. An example of this is with search assistants on a mobile device. There are various additional reasons this is a beneficial subfield of artificial intelligence, including:

  • It reinforces computer and programming code which AI applications, such as robotics, rely on to function.

  • It creates interactive environments for agents to build frameworks for future behaviours.

  • It establishes procedural standards for technical and digital systems to follow.

Components of learning by reinforcement

Machine learning that applies reinforcement parameters comprises an agent and the environment in which it performs. Aside from these two elements, there are various additional components which can be fundamental to intelligent learning systems, including:

  • Rewards: Rewards in engineering establish goals for reinforcement problems, wherein an agent receives a reward signal when achieving desired outcomes.

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

  • Policies: This element of machine learning uses policies to define the behaviour of an agent throughout a defined period. The policy engineers implement maps the state of the environment to the action and the action to the agent's behaviour within the environment.

  • Environment model: Occasionally, systems use models of the environment to recreate behaviours particular to the environment, allowing engineers to make inferences about how environments may react to agents.

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Types of learning by reinforcement

In this type of machine learning, engineers can either apply negative or positive learning strategies that train agents and environments to repeat favourable actions. Positive reinforcement is the result of agents taking a defined set of actions or behaving in a certain way. This method works to increase the frequency of the desired behaviour exhibited by an agent. It also directs the agent by approving its actions, making it more likely that it repeats its behaviour.

Conversely, negative reinforcement works to discourage undesirable behaviours or actions, which engineers train the agent to avoid. Negative reinforcements communicate to agents and environments what the minimum performance standard is, which results in agents learning from this feedback to meet the standard set by engineers. This contrasts positive reinforcement by telling the agent what not to do, rather than rewarding desirable actions.

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What processes does learning by reinforcement follow?

Data entered into an agent moves through an environment to perform a set of actions. When the actions are desirable, programmers may reward an agent to reinforce the actions it performed. When the agent performs incorrect actions, programmers reconfigure new parameters to prevent those actions from happening again. In the context of rewarding good behaviour, this works much like a punishment for behaving badly. Here, a programmer reconfigures sophisticated software code which allows agents to recognize incorrect behaviour and avoid it in the future. Through this mechanism of reward and punishment, programmers reinforce the learning of their agents.

Drawbacks of learning by reinforcement

While learning through reinforcement is beneficial for many applications using independent artificial intelligence systems, this model does come with certain challenges. Engineers and programmers commonly encounter these problems and solve them to make their AI systems functional and optimize their operation. Some challenges programmers commonly overcome may include:

  • Limited modelling: As this machine learning field uses the Markov model of training through reinforcement, it may lead to limitations in sequential reasoning, probability calculations, and event modelling.

  • State overload: With learning through positive reinforcement, an abundance of reinforcement can lead to state overload, meaning that the environmental state is too overloaded with input information, diminishing the results of the output.

  • Heavy reliance on data: This machine learning field can be more compatible with solving complex problems than simple ones, as it requires large amounts of data for environments and its agents to perform.

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Applications of learning by reinforcement

This model of learning is efficient when training artificial intelligence systems to achieve outcomes by themselves. The real-world application of this software training mechanism is broad and influences many technologies that many people use daily. Below are descriptions of a few of the common applications of machine learning through the reinforcement model:


Videogames and computer games commonly use AI systems and often implement reinforcement as a learning model to train programs. This learning model helps to teach agents in artificial intelligence environments how to behave and which actions to perform to add functionality to the game. For instance, in an online board game, players may compete against an AI program. In this case, programmers may have used reinforcing learning practices to train the program to take actions to defeat the human player.


In industrial automation and robotics, programmers and engineers use this learning model to enable a robot to establish a functional control system for itself by learning from its own behaviour and experiences. With complex systems such as robotics control networks, learning through reinforcement can help AI programs to gain new functionality. They also gain more adaptability and versatility in the way they respond to their programming environment and the real-world environment.

Text engines

Uses of machine learning through reinforcement also include abstractive text summarization engines and dialogue agents, such as those which translate text to speech. These learn from user interactions, and through this feedback improve over time. Through reinforcement, they learn which results satisfy users and which don't, and improve their functionality by responding to this input. Similarly, language translation services may implement a reinforced learning model to optimize the accuracy of their AI programs.

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Differences between reinforcement and supervised learning

Supervised and reinforcement learning are both machine learning subfields which rely on deep learning processes to interpret data input and achieve desired results. While both subfields are similar, there are various key differences in how programmers and engineers complete processes in their environments. Below are descriptions of how each learning system works in relation to the other:

Reinforcement learning

With learning through reinforcement, in contrast to supervised learning, the interactions between environments and agents happen in small steps to achieve their exploration and learning of tasks. This leads to defined pathways for agents to achieve results. These results typically have the following qualities:

  • The policy trains the agent to perform particular actions to maximize the accumulation of rewards from its environment.

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

  • Parameters use elements of value, reward, action, and next-state procedures to develop policies to train the neural network model.

Supervised learning

Supervised learning differs from the reinforcement model in that it performs either regression or classification tasks to evaluate and establish training data. This training data goes on to produce generalized outputs. Supervised learning achieves distinct pairs of input and output values, with which the environment uses several algorithms to perform particular actions. Rather than relying on decision-making processes and mathematical modelling frameworks, supervised learning processes use:

  • Performance analysis to determine the trained model's functionality, efficiency, and ability to reach desired goals

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

  • Parameters of training from the dataset to direct neural networks to map data to respective labels

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