What is reinforcement learning commonly defined as?

Reinforcement learning is commonly defined as learning through interaction and feedback because it emphasizes the role of an agent making decisions in an environment to achieve a goal. In this framework, the agent takes actions, observes the results of those actions, and receives feedback in the form of rewards or penalties. This feedback helps the agent understand which actions lead to positive outcomes, thus guiding future decision-making. The core idea is that the learning process is driven by the agent’s experiences and the consequences of its actions, which is fundamental to the reinforcement learning paradigm.

Learning through observation, often associated with supervised learning, does not capture the interactive nature and the importance of feedback inherent in reinforcement learning. Similarly, learning by trial and error might imply a passive approach to learning when in reality, reinforcement learning is about actively engaging with the environment and adjusting strategies based on real-time feedback. Lastly, learning from expert input aligns more with supervised methods rather than the self-directed exploration found in reinforcement learning. This distinction highlights the unique characteristics of reinforcement learning focused on interaction and the responses that shape learning over time.