  Learning how to act when there are many available actions in each state is a
challenging task for Reinforcement Learning (RL) agents, especially when many
of the actions are redundant or irrelevant. In such cases, it is sometimes
easier to learn which actions not to take. In this work, we propose the
Action-Elimination Deep Q-Network (AE-DQN) architecture that combines a Deep RL
algorithm with an Action Elimination Network (AEN) that eliminates sub-optimal
actions. The AEN is trained to predict invalid actions, supervised by an
external elimination signal provided by the environment. Simulations
demonstrate a considerable speedup and added robustness over vanilla DQN in
text-based games with over a thousand discrete actions.
