"Learning from interaction" in reinforcement learning (RL) refers to the process where an agent learns how to make decisions and improve its performance by interacting with an environment. Unlike supervised learning, where the model is trained on a fixed dataset, the RL agent learns by taking actions in the environment, observing the outcomes, and adjusting its behavior based on the received rewards. The agent learns through trial and error, continuously refining its policy to maximize long-term rewards. For example, in a game, an agent might learn how to play by trying different moves, receiving rewards based on its performance, and adjusting its strategy accordingly.
This interactive learning process allows the agent to adapt to dynamic environments where the optimal strategy may change based on feedback. The more the agent interacts with the environment, the better it can learn the relationships between states, actions, and rewards.
Overall, learning from interaction is fundamental in RL because it enables agents to improve through experience, making it possible to tackle complex decision-making tasks.