this action space representation include conversational agents such as personal assistants [@dhingra2016end; @li2017end; @su2016continuously; @lipton2016efficient; @liu2017end; @zhao2016towards; @wu2016google], travel planners [@peng2017composite], restaurant/hotel bookers [@budzianowski2017sub], chat-bots [@serban2017deep; @li2016deep] and text-based game agents [@DBLP:journals/corr/NarasimhanKB15; @DBLP:journals/corr/HeCHGLDO15; @zelinka2018using; @yuan2018counting; @cote2018textworld]. RL is currently being applied in all of these domains, facing new challenges in function approximation and exploration due to the larger action space.

In this work, we propose a new approach for dealing with large action spaces that is based on *action elimination*; that is, restricting the available actions in each state to a subset of the most likely ones (Figure \[fig:diagram\]). We propose a method that eliminates actions by utilizing an elimination signal; a specific form of an auxiliary reward [@jaderberg2016reinforcement], which incorporates domain-specific knowledge in text games. Specifically, it provides the agent with immediate feedback regarding taken actions that are not optimal. In many domains, creating an elimination signal can be done using rule-based systems. For example, in parser-based text games, the parser gives feedback regarding irrelevant actions *after* the action is played (e.g., Player: “Climb the tree.” Parser: “There are no trees to climb”). Given such signal, we can train a machine learning model to predict it and then use 