Continuing tasks in reinforcement learning (RL) are tasks where the agent interacts with the environment continuously, without a predefined end or terminal state. In these tasks, the agent's objective is to maximize long-term rewards over an indefinite period. The task doesn't have a natural end, so the agent’s learning process continues as long as it remains active in the environment.
An example of a continuing task could be a robot that needs to maintain a balanced state, such as a self-balancing robot or a stock trading agent. In such tasks, the agent continuously interacts with the environment and receives rewards, but there is no terminal state to signify the end of the task.
Unlike episodic tasks, continuing tasks require the agent to learn strategies that are sustainable over time, balancing short-term and long-term rewards to ensure its behavior remains optimal in a continuous environment.