Episodic tasks in reinforcement learning (RL) are tasks where the agent's interactions with the environment are divided into discrete episodes. Each episode begins with an initial state and ends when a terminal state is reached, such as the agent completing a goal or failing a task. The agent receives rewards throughout the episode, and its objective is to maximize the total reward across all episodes.
An example of an episodic task is playing a game, where each match or round is an episode. The agent's goal is to learn strategies that will lead to the highest cumulative score within each episode. At the end of each episode, the agent starts from a new initial state and tries to improve based on previous experience.
Episodic tasks are useful in RL because they provide a clear structure for learning, with each episode providing a self-contained learning experience. The agent can evaluate its performance at the end of each episode and refine its strategy for the next episode.