highest $Q$-value by taking an $\arg\max$ on $Q$-values among $A',$ and if it chooses to explore, then, it selects an action uniformly from $A'$. **Targets()** estimates the value function by taking $\max$ over $Q$-values only among admissible actions, hence, reducing function approximation errors.\
**Architectures:** The agent uses an Experience Replay [@lin1992self] to store information about states, transitions, actions, and rewards. In addition, our agent also stores the elimination signal, provided by the environment (Figure \[fig:diagram\]). The architecture for both the AEN and DQN is an NLP CNN, based on [@kim2014convolutional]. We represent the state as a sequence of words, composed of the game descriptor (Figure \[ZRK\_ENV\], “Observation”) and the player’s inventory. These are truncated or zero-padded (for simplicity) to a length of 50 (descriptor) + 15 (inventory) words and each word is embedded into continuous vectors using *word2vec* in $\mathbb{R}^{300}$. The features of the last four states are then concatenated together such that our final state representations $s$ are in $\mathbb{R}^{78,000}$. The AEN is trained to minimize the MSE loss, using the elimination signal as a label. We used $100$ ($500$ for DQN) convolutional filters, with three different 1D kernels of length (1,2,3) such that the last hidden layer size 