is $300.$ [^3]

Experimental Results
====================

**Grid World Domain:** We start with an evaluation of action elimination in a small grid world domain with 9 rooms, where we can carefully analyze the effect of action elimination. In this domain, the agent starts at the center of the grid and needs to navigate to its upper-left corner. On every step, the agent suffers a penalty of $(-1)$, with a terminal reward of $0$. Prior to the game, the states are randomly divided into $K$ categories. The environment has $4K$ navigation actions, 4 for each category, each with a probability to move in a random direction. If the chosen action belongs to the same category as the state, the action is performed correctly with probability $p_c^T=0.75$. Otherwise, it will be performed correctly with probability $p_c^F=0.5$. If the action does not fit the state category, the elimination signal equals $1$, and if the action and state belong to the same category, then it equals $0$. An optimal policy only uses the navigation actions from the same type as the state, as the other actions are clearly suboptimal. We experimented with a vanilla Q-learning agent without action elimination and a tabular version of action 