Reviews: Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning

This paper addresses the challenge of an environment with discrete, but large number, of actions, by eliminating the actions that are never taken in a particular state. To do so, the paper proposes AE-DQN which augments DQN with contextual multi-armed bandit to identify actions that should be eliminated. Evaluation conducted on a text-based game, Zork, shows promising results, as AE-DQN outperforms baseline DQN on several examples. This idea of eliminating actions which are never taken in a given state is a sound on. The paper is clear and well written.