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We consider the task of learning to play families of text-based computeradventure games, i.e., fully textual environments with a common theme (e.g.cooking) and goal (e.g. prepare a meal from a recipe) but with differentspecifics; new instances of such games are relatively straightforward forhumans to master after a brief exposure to the genre but have been curiouslydifficult for computer agents to learn. We find that the deep Q-learningstrategies that have been successfully leveraged for superhuman performance insingle-instance action video games can be applied to learn families of textvideo games when adopting simple strategies that correlate with human-likelearning behavior. Specifically, we build agents that learn to tackle simplescenarios before more complex ones using curriculum learning, that familiarizethemselves in an unfamiliar environment by navigating before acting, and thatexplore uncertain environments more thoroughly using contextual multi-armedbandit decision policies. We demonstrate improved task completion rates overreasonable baselines when evaluating on never-before-seen games of that theme.

Learn How to Cook a New Recipe in a New House: Using Map  Familiarization, Curriculum Learning, and Bandit Feedback to Learn Families  of Text-Based Adventure Games