Reinforcement Learning Is Too Narrow for Exploration

Reinforcement learning, where an agent takes actions and learns from whether they make it better off (more reward), played a key role in solving games like Go, but it is too narrow for genuine world-understanding because an agent solely tracking whether it is better off will not perform the exploration needed to figure out how the world really works.