Reinforcement learning (RL) is a dynamic and evolving subfield of machine learning that focuses on training intelligent agents to learn and adapt through interactions with their environment. This introductory article provides an overview of the fundamental concepts and principles of RL, elucidating its core components, such as the agent, environment, actions, and rewards. This study aims to give readers an in-depth introduction to RL and show examples of its different uses in various domains. RL can allow agents to learn through interaction with an environment, which has led to its enormous interest. The core ideas of RL and its essential elements will be covered in this study, after which it will go into applications in industries including robotics, gaming, finance, healthcare, and more. The fundamental ideas of RL will become clearer to readers, and they will recognize how transformative it can be when used to address challenging decision-making issues. These applications demonstrate the versatility and significance of RL in shaping the future of technology and automation.
TopIntroduction/Preliminaries
RL is a specialized field within machine learning that is primarily oriented towards solving control problems. It amalgamates the benefits of dynamic programming with a trial-and-error approach. In RL, an agent-based control paradigm is adopted, wherein the agent learns by interacting with the controlled environment. RL draws inspiration from the natural learning process that occurs through interactions with the environment, mirroring how biological systems learn (Amin et al. 2023) (Sutton and Barto 2018). Like other forms of learning, it revolves around establishing connections between states and actions to optimize specific rewards. Nevertheless, the primary challenge in this type of learning lies in the fact that, unlike conventional machine learning approaches, the learner must autonomously discover the optimal actions for specific situations. Consequently, a learning agent needs to comprehend the environment, select actions that maximize rewards, and adapt its behavior accordingly, even in the face of environmental uncertainties. RL systems are well-suited for unsupervised, real-time implementation, as they construct their understanding of the environment through exploration. Figure 1 illustrates a general RL framework. The formalization of RL is achieved through the utilization of a Markov decision process (MDP), which serves as a discrete-time stochastic control process (Amin et al. 2023). MDP offers a structured mathematical foundation for modeling the decision-making process within this framework.
Figure 1. Representation of RL structure
The learner or decision-maker interacts with the environment. The agent observes the current state, selects actions, and receives feedback from the environment.
The environment's behavior can be described by the state it assumes at a given time, denoted as S(t), which is characterized by a set of attributes or values. Each state is associated with a reward or immediate cost, represented as R(t), that is generated upon entering that state. At each time step, the agent has a choice of taking one of several possible actions, denoted as A(t), which influence the subsequent state of the system, S(t + 1), and consequently the rewards or costs experienced, with probabilities governing these transitions. The agent's decision-making process, considering the current state, is shaped by its past experiences. In this manner, an RL system utilizes its history of actions in specific states and the corresponding rewards to update its strategy for future actions. Over time, the agent evolves a policy for selecting actions based on the state of the system during its interactions with the environment (Sutton and Barto 2018).
A representation of the environment at a specific time. States can be as simple as raw sensory data or abstract representations, depending on the problem. In RL, the agent typically makes decisions based on the current state.
The set of possible choices or decisions the agent can make at each state. Actions can be discrete or continuous, depending on the problem. The agent selects actions to transition from one state to another.
A scalar value that provides feedback to the agent after each action. The reward signal quantifies the immediate desirability or quality of an action taken in a particular state. The agent's objective is to maximize the cumulative reward over time.