# A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective
## 3 The Quintessence of Reinforcement Learning  
Artificial Intelligence (AI) has become an integral and significant aspect of our daily life as we approach the conclusion of this decade. Artificial Intelligence has been present for the past seven decades but has gained significant momentum in the recent three decades, leading to extensive research in this field [39]. AI refers to various techniques that allow computers to acquire knowledge and make choices by analyzing data. Out of these techniques, machine learning (ML) has made significant advancements in the past two decades, transitioning from a mere curiosity in laboratories to a widely used practical technology in commercial applications. In AI, ML has become the preferred approach for creating functional software in areas such as computer vision, natural language processing, speech recognition, robot control, and various other applications [40]. Many AI developers now acknowledge that, in several cases, it is more convenient to train a system by demonstrating ideal input-output behavior rather than manually programming it to anticipate the desired response for every potential input. ML has significantly impacted various areas of computer science and sectors that deal with data-driven problems. It includes consumer services, diagnosing defects in intricate systems, and managing logistics chains. ML approaches have had diverse effects on several empirical sciences, such as biology, cosmology, and social science. These approaches have been used creatively to examine vast experimental data [41]. Figure 1: Research methodology framework. Three major ML paradigms are supervised learning, unsupervised learning, and reinforcement learning [15]. Supervised learning entails the process of training a model using a dataset that has been labeled, meaning that each input is associated with its corresponding accurate output. The objective is to acquire knowledge about a transformation from given inputs to corresponding outputs, which can then be utilized to make accurate forecasts on novel, unobserved data. Unsupervised learning involves the identification of concealed patterns or inherent structures in unlabeled input data. The model aims to acquire knowledge about the fundamental distribution or grouping of the data without explicit instructions on the desired outcome [28]. RL is centered around teaching an agent to make numerous choices by engaging with an environment and receiving rewards or punishments as feedback. The agent acquires the ability to optimize its activities to maximize the total rewards it receives over a period [42]. Due to its dynamic and adaptive methodology, RL is highly effective for addressing problems in the manufacturing systems and robotics research domain [22; 28]. RL is distinct from supervised learning, where a model is trained on a predetermined dataset, and unsupervised learning, where the model discovers concealed patterns within data. In reinforcement learning, an agent is educated to make a series of decisions by engaging with an environment. The agent's objective is to acquire a strategy, sometimes known as a policy, that optimizes the total rewards obtained over time [43]. RL is a decision-making process influenced by behavioral psychology. It involves an agent learning to achieve a goal by acting and receiving feedback from the environment through rewards or penalties. This trial-and-error method is akin to how humans and animals learn from their environment. The critical components of RL are the agent, the environment, actions, states, and rewards. The agent observes the environment, takes actions, and receives rewards, which are used to guide future actions [14]. RL is particularly effective in tasks with uncertain outcomes, such as playing games, controlling robots, and managing resources. In these situations, the agent must balance exploration (trying new actions) and exploitation (using established actions that lead to significant rewards) to develop an optimal policy efficiently [42].