In reinforcement learning, what is a complete sequence of interactions between an agent and its environment known as?

In reinforcement learning, a complete sequence of interactions between an agent and its environment is referred to as an episode. During an episode, the agent takes actions, receives feedback in the form of rewards or penalties from the environment, and updates its policy based on the gathered experiences. This sequence typically continues until a specific condition is met, such as reaching a terminal state or achieving a defined goal.

Understanding the concept of an episode is crucial because it helps delineate the learning trajectories and performance evaluation of the agent. Each episode provides valuable insights into how well the agent is learning to navigate its environment and optimize its decision-making process based on past experiences.

The other terms listed, while related to reinforcement learning, do not accurately define the complete sequence of interactions. An epoch generally refers to a complete pass through the training dataset in supervised learning contexts; environment simply denotes the setup in which the agent operates; and a cycle might imply repeated actions but does not capture the entirety of interactions like an episode does.