DQN to explore and learn Q-values only for valid actions.

We tested our method in a text-based game called “Zork”. This game takes place in a virtual world in which the player interacts with the world through a text-based interface (see Figure \[ZRK\_ENV\]). The player can type in any command, corresponding to the in-game action. Since the input is text-based, this yields more than a thousand possible actions in each state (e.g., “open door”, “open mailbox” etc.). We demonstrate the agent’s ability to advance in the game faster than the baseline agents by eliminating irrelevant actions.

Related Work
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**Text-Based Games (TBG):** Video games, via interactive learning environments like the Arcade Learning Environment (ALE) [@bellemare2013arcade], have been fundamental to the development of DRL algorithms. Before the ubiquitousness of graphical displays, TBG like Zork were popular in the adventure gaming and role-playing communities. TBG present complex, interactive simulations which use simple language to describe the state of the environment, as well as reporting the effects of player actions (See Figure \[ZRK\_ENV\]). Players interact with the environment through text commands that respect a predefined grammar, which must be discovered in each game.

TBG provide a testbed for research at the intersection of 