---
abstract: '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.'
author:
- |
    Tom Zahavy$^{*1,2}$, Matan Haroush$^{*1}$, Nadav Merlis$^{*1}$, Daniel J. Mankowitz$^{3}$, Shie Mannor$^1$\
    $^1$The Technion - Israel Institute of Technology, $^2$ Google research, $^3$ Deepmind\
- '\* Equal contribution'
- 'Corresponding to `\{tomzahavy,matan.h,merlis\}@campus.technion.ac.il`'
bibliography:
- 'iclr2018\_workshop.bib'
title: 'Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning'
---

Introduction
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Learning control policies for sequential decision-making tasks where both the state space and the action space are vast is critical when applying Reinforcement Learning (RL) to real-world problems. This is because there is an exponential 