What term describes the external system or context with which an agent interacts, providing states and rewards based on the agent's actions?

The term that describes the external system or context with which an agent interacts, providing states and rewards based on the agent's actions is called the environment. In the context of artificial intelligence and reinforcement learning, the environment encompasses everything that influences the agent's decision-making process. It provides the conditions under which the agent operates, including the states it can encounter and the rewards it receives for its actions, thereby guiding the learning and adaptation of the agent.

Other terms such as episode, datum, and model have specific meanings that do not fit this definition. An episode refers to a single sequence of states and rewards, from an initial state to a terminal state, typically used to analyze the learning performance. Datum refers to a singular piece of data, which does not encompass the broader interactive context. A model, on the other hand, is a representation or simulation of the environment or the agent's behavior within the environment rather than the environment itself. Thus, the environment is the appropriate term that encapsulates the functioning dynamics between the agent and its external context.