The predator can be user-controlled, allowing the flock to possibly be herded into a pen. A strategy can be formed to perform such an action through interacting with the tadpoids, but as the movement of the flock is unpredictable the user has to constantly re-evaluate their strategy. A flock that emerges from the sum of the individual tadpoids is a complex uncertain environment. The predator is the learning agent whose overall goal is to pen the tadpoids. It would do so by learning from interaction, which is a fundamental idea underlying nearly all theories of learning and intelligence.
Reinforcement learning focuses on goal-directed learning from interaction.
The basic idea is simply to capture the most important aspects of the real problem facing a learning agent interacting with its environment to achieve a goal. Such an agent must be able to sense the state of the environment to some extent and must be able to take actions that affect the state. The agent must have a goal or goals relating to the state of the environment. The task of learning to herd a flock appears suited to reinforcement learning as there is not a fixed way of performing the task. The flock is constantly changing and so only by experience with interactions with the tadpoids can the predator improve its performance.