Interactive Fiction Games: A Colossal Adventure

Abstract

A hallmark of human intelligence is the ability to understand and communicate with language. Interactive Fiction games are fully text-based simulation environments where a player issues text commands to effect change in the environment and progress through the story. We argue that IF games are an excellent testbed for studying language-based autonomous agents. In particular, IF games combine challenges of combinatorial action spaces, language understanding, and commonsense reasoning. To facilitate rapid development of language-based agents, we introduce Jericho, a learning environment for man-made IF games and conduct a comprehensive study of text-agents across a rich set of games, highlighting directions in which agents can improve.

Transfer in Deep Reinforcement Learning Using Knowledge Graphs

“…We test our system on two separate sets of games in different domains using the Jericho and TextWorld frameworks (Hausknecht et al, 2019a;. The first set of games is "slice of life" themed and contains games that involve mundane tasks usually set in textual descriptions of normal houses.…”

Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning

“…(3) The text commands to control characters are less restricted, having sizes over six orders of magnitude larger than previous text games. The recently introduced Jericho benchmark provides a collection of such IF games (Hausknecht et al, 2019a). The complexity of IF games demands more sophisticated NLU techniques than those used in synthetic text games.…”

“…To make RL agents learn efficiently without prohibitive exhaustive trials, the action estimation must generalize learned knowledge from tried actions to others. To this end, previous approaches, starting with a single embedding vector of the observation, either predict the elements of actions independently (Narasimhan et al, 2015;Hausknecht et al, 2019a); or embed each valid action as another vector and predict action value based on the vector-space similarities (He et al, 2016). These methods do not consider the compositionality or role-differences of the action elements, or the interactions among them and the observation.…”

“…Previous work mainly studies the text understanding and generation in parserbased or rule-based text game tasks, such as TextWorld platform (Côté et al, 2018) or custom domains (Narasimhan et al, 2015;He et al, 2016;Adhikari et al, 2020). The recent platform Jericho (Hausknecht et al, 2019a) supports over thirty human-written IF games. Earlier successes in real IF games mainly rely on heuristics without learning.…”

“…LSTM-DQN (Narasimhan et al, 2015) was proposed to generate verb-object action with pre-defined sets of possible verbs and objects, but treat the selection and learning of verbs and objects independently. Template-DQN (Hausknecht et al, 2019a) extended LSTM-DQN for template-based action generation, introducing one additional but still independent prediction output for the second object in the template. Deep Reinforcement Relevance Network (DRRN) (He et al, 2016) was introduced for choice-based games.…”