However, for $A_2$, with T=100, (Figure \[fig:egg\]$b$) the AE-DQN agent learns considerably faster, implying that action elimination is more robust to hyperparameters settings when there are many actions.

[r]{}[0.4]{} ![image](zork4.png){width="1.05\linewidth"} \[troll\]

**The Troll Quest:** In this quest, the agent must find a way to enter the house, grab a lantern and light it, expose the hidden entrance to the underworld and then find the troll, awarding him 100 points. The Troll Quest presents a larger problem than the Egg Quest, but smaller than the full Zork domain; it is large enough to gain a useful understanding of our agents’ performance. The AE-DQN (blue) and DQN (green) agents use a similar action set to $A_1$ with $200$ take actions and $15$ necessary actions (215 in total). For comparison, We also included an “optimal elimination” baseline (red) that consists of only $35$ actions (15 essential, and 20 relevant take actions). We can see in Figure \[troll\] that AE-DQN significantly outperforms DQN, achieving compatible performance to the “optimal elimination” baseline. In addition, we can see that the improvement of the AE-DQN over DQN is more significant in the Troll Quest than the Egg quest. This observation is consistent with our tabular experiments.

[r]{}[0.4]{}

