What is meant by a reinforcement learning agent?

A reinforcement learning agent is defined by its goal of maximizing cumulative rewards through interactions with its environment. In reinforcement learning, the agent takes actions within a defined state space and receives feedback in the form of rewards or penalties, which signal the effectiveness of those actions in achieving its objectives.

This learning paradigm is characterized by trial-and-error methods, where the agent evaluates the consequences of its actions and learns policies or strategies that increase the likelihood of obtaining higher rewards over time. Such agents are designed to adapt and improve their decision-making processes based on past experiences, which allows them to navigate complex environments effectively.

The other options describe different concepts. While a system that automates learning processes refers to broader machine learning principles, it doesn’t specifically denote the reward-based approach of reinforcement learning. Additionally, a human supervisor guiding the learning process is more relevant to supervised learning scenarios, where clear labeled data is provided. Finally, traditional machine learning models typically include supervised or unsupervised learning techniques, lacking the active exploration and learning mechanism inherent in reinforcement learning.