Who or what is defined as an agent in reinforcement learning?

In reinforcement learning, an agent is defined as an entity that interacts with its environment in order to maximize cumulative rewards over time. This agent makes decisions based on the current state of the environment and learns to choose actions that lead to the most favorable outcomes, effectively optimizing its performance through experience. The learning process involves the agent receiving feedback in the form of rewards or penalties based on the actions it takes, which guides it to improve its decision-making over time.

The role of the agent is central to reinforcement learning as it embodies the concept of learning through trial and error, adapting its strategy based on the successes and failures it encounters. This definition emphasizes the proactive nature of the agent within the reinforcement learning framework, focusing on the relationship between the agent and its surrounding environment to achieve specific goals.

In contrast, other options reference components that do not encapsulate the core function of an agent in this context. For instance, a software that generates data is more about data processing than interaction and learning, while a large dataset is merely a resource used for training but does not embody the decision-making qualities of an agent. An algorithm that predicts outcomes describes a different aspect of machine learning, focusing on prediction rather than the exploratory and reward-seeking behavior characteristic of reinforcement learning agents