From Value Functions to Policy Search: How Modern AI Systems Learn to Act Under Uncertainty
From Value Functions to Policy Search: Rethinking Decision-Making in Modern AI Systems In most traditional AI systems, decision-making is indirect. A model predicts an outcome, another layer interprets that prediction, and finally, a rule or heuristic decides what to do. This pipeline introduces complexity and latency. A different paradigm is emerging: What if systems could learn how to act directly—without first learning how to evaluate every possible state? This is the core idea behind policy search . The Shift: From Evaluating States to Learning Actions Classical decision-making follows a sequential dependency: State \(\rightarrow\) Value Function (\(V\)) \(\rightarrow\) Action (\(a\)) Policy search simplifies the runtime architecture by mapping observations directly to behavior: State \(\rightarrow\) Policy (\(\pi\)) \(\rightarrow\) Action (\(a\)) Instead of estimating value, we optimize a function that dictates b...