Task: Question Answering
Input (English): We first introduce a method that detects bottlenecks in text-games using the overall reward gained and the knowledge graph state. This method freezes the policy used to reach the bottleneck and restarts the training from there on out, additionally conducting a backtracking search to ensure that a sub-optimal policy has not been frozen. The second contribution explore how to leverage knowledge graphs to improve existing exploration algorithms for dealing with combinatorial action-spaces such as Go-Explore BIBREF9.  
 Question: What are the two new strategies?
Output (English): a method that detects bottlenecks in text-games using the overall reward gained and the knowledge graph state to leverage knowledge graphs to improve existing exploration algorithms for dealing with combinatorial action-space