In
reinforcement learning (RL), an agent is the entity responsible for making decisions and taking actions within an environment to achieve a certain objective or goal. The agent operates based on its observations of the environment and the feedback it receives in the form of rewards or penalties.
Here are the key components of an RL agent :
Perception : The agent perceives the current state of the environment through sensors or observations. These observations provide information about the environment's current conditions, including relevant features, objects, or properties.
Decision-making : Based on its perception of the environment, the agent selects actions to execute. These actions are chosen according to a decision-making process, often guided by the agent's current policy, which determines the mapping from states to actions.
Learning : The agent learns from its interactions with the environment over time. It aims to improve its decision-making abilities by adjusting its policy based on the feedback it receives from the environment, typically in the form of rewards or punishments.
Goal-seeking : The agent has a predefined objective or goal that it seeks to achieve through its actions. This goal might be explicitly specified by a designer or implicitly defined by the nature of the task.
Exploration and Exploitation : The agent balances exploration of new actions and exploitation of known actions to maximize its long-term rewards. This trade-off ensures that the agent continues to learn and discover optimal strategies while also leveraging its current knowledge to achieve immediate rewards.
RL agents can vary in complexity and sophistication, ranging from simple rule-based systems to complex neural network-based models. They are central to the field of reinforcement learning, driving advancements in various applications such as game playing, robotics, recommendation systems, and autonomous vehicles.
