In reinforcement learning (RL), “learning from interaction” means an agent improves its decision-making by actively experimenting in an environment, observing outcomes, and adjusting its behavior based on feedback. Unlike supervised learning, where models learn from static labeled datasets, RL agents learn through trial and error. The agent takes actions, receives rewards or penalties, and uses this feedback to update its strategy (policy) over time. This process mirrors how humans learn skills like riding a bike: you try actions, notice what works, and refine your approach through repeated attempts.
The core mechanism involves three components: the agent, the environment, and the feedback loop. For example, consider a robot learning to navigate a maze. The agent (robot) starts by moving randomly (exploration). Each action (e.g., turning left) changes the robot’s state (position) and generates a reward (e.g., +1 for moving closer to the exit, -1 for hitting a wall). Over time, the agent builds a policy that maps states to actions likely to maximize cumulative rewards. This requires balancing exploration (trying new actions) and exploitation (using known successful actions). The agent might use algorithms like Q-learning to iteratively update a table (Q-table) storing the expected value of actions in specific states.
Practical challenges arise because feedback is often delayed or sparse. For instance, a game-playing AI might only receive a reward at the end of a 10-minute match, making it hard to link specific actions to outcomes. Techniques like temporal difference learning help by breaking long-term rewards into smaller, incremental updates. Additionally, environments with complex state spaces (e.g., autonomous driving) require function approximation (like neural networks) to generalize from limited interactions. These examples highlight how RL systems must handle noisy, real-time data while continuously adapting—key reasons why interaction-driven learning is both powerful and computationally demanding.