Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: Solving sequential decision making problems, such as text parsing, robotic<lb>control, and game playing, requires a combination of planning policies and gen-<lb>eralisation of those plans. In this paper, we present Expert Iteration, a novel al-<lb>gorithm which decomposes the problem into separate planning and generalisation<lb>tasks. Planning new policies is performed by tree search, while a deep neural net-<lb>work generalises those plans. In contrast, standard Deep Reinforcement Learning<lb>algorithms rely on a neural network not only to generalise plans, but to discover<lb>them too. We show that our method substantially outperforms Policy Gradients in<lb>the board game Hex, winning 84.4% of games against it when trained for equal<lb>time.

Thinking Fast and Slow with Deep Learning and Tree Search