elimination Q-learning. Our simulations show that action elimination dramatically improves the results in large action spaces. In addition, we observed that the gain from action elimination increases as the amount of categories grows, and as the grid size grows, since the elimination allows the agent to reach the goal earlier. We have also experimented with random elimination signal and other modifications in the domain. Due to space constraints, we refer the reader to the appendix for figures and visualization of the domain.

![image](dungeon.jpg){width="80.00000%"}

**Zork domain:** *“This is an open field west of a white house, with a boarded front door. There is a small mailbox here. A rubber mat saying ’Welcome to Zork!’ lies by the door”*. This is an excerpt from the opening scene provided to a player in “Zork I: The Great Underground Empire”; one of the first interactive fiction computer games, created by members of the MIT Dynamic Modeling Group in the late 70s. By exploring the world via interactive text-based dialogue, the players progress in the game. The world of Zork presents a rich environment with a large state and action space (Figure  \[ZRK\_WORLD\]).\
Zork players describe their actions using natural language instructions. For example, in 