Robot Learning

Exploration-Exploitation Tradeoff

Exploration-Exploitation Tradeoff is the fundamental dilemma of sequential decision-making: an agent must balance exploiting the best-known action against exploring alternatives that might prove better, since every exploratory choice forgoes known reward while every exploitative choice forgoes information. Formalized in the multi-armed bandit literature, principled resolutions include upper confidence bounds, Thompson sampling, and entropy-regularized objectives.

Why it matters for physical AI

Deployed robots face the tradeoff operationally: a fleet can keep executing its proven behavior or trial updated policies that might fail, making bandit-style evaluation strategies relevant to continuous fleet improvement.