What is an episode in Reinforcement Learning (RL)?

An episode in Reinforcement Learning refers to a complete sequence of states, actions, and rewards that occurs from the start of an interaction with the environment until a termination condition is met. This could involve reaching a goal, running out of time, or any other stopping criterion. During an episode, the agent explores its environment and learns from the outcomes of its actions, forming a basis for improving its future decision-making.

By focusing on B, it becomes clear that an episode encapsulates the entire experience of the agent within that specific trial, including all transitions between states due to its actions and the corresponding rewards received. This is essential for an agent's learning process since it helps in understanding the consequences of actions over time and in adjusting the policy based on the accumulated experiences.

In contrast, the other options don't fully capture the concept of an episode. While a series of actions taken by the agent might seem relevant, it does not include the states and rewards that are critical for learning. Evaluating the agent's performance refers to assessing its behavior over episodes rather than defining what an episode is. Lastly, defining an episode as a single state misses the essence of what an episode entails, which is the complete journey from one state to another, encompassing all pertinent interactions.