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

Understand

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.