Self-Supervised Behavior Cloned Transformers are Path Crawlers for Text
  Games
Ruoyao Wang Peter Jansen
In this work, we introduce a self-supervised behavior cloning transformer for
text games, which are challenging benchmarks for multi-step reasoning in
virtual environments. Traditionally, Behavior Cloning Transformers excel in
such tasks but rely on supervised training data. Our approach auto-generates
training data by exploring trajectories (defined by common macro-action
sequences) that lead to reward within the games, while determining the
generality and utility of these trajectories by rapidly training small models
then evaluating their performance on unseen development games. Through
empirical analysis, we show our method consistently uncovers generalizable
training data, achieving about 90\% performance of supervised systems across
three benchmark text games.