As this is too large a space for a RL agent to effectively explore, the knowledge graph is used to prune this space by ranking actions based on their presence in the current knowledge graph and the relations between the objects in the graph as in <cite>(Ammanabrolu and Riedl, 2019)</cite> The architecture for the deep Q-network consists of two separate neural networks-encoding state and action separately-with the final Q-value for a state-action pair being the result of a pairwise interaction function between the two (Figure 1 ).