Curriculum learning, room-aware action labels, and LinUCB exploration improve zero-shot performance on TextWorld cooking game families, reaching 72% and 68% of achievable points.
Language Expansion In Text-Based Games
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abstract
Text-based games are suitable test-beds for designing agents that can learn by interaction with the environment in the form of natural language text. Very recently, deep reinforcement learning based agents have been successfully applied for playing text-based games. In this paper, we explore the possibility of designing a single agent to play several text-based games and of expanding the agent's vocabulary using the vocabulary of agents trained for multiple games. To this extent, we explore the application of recently proposed policy distillation method for video games to the text-based game setting. We also use text-based games as a test-bed to analyze and hence understand policy distillation approach in detail.
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cs.CL 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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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
Curriculum learning, room-aware action labels, and LinUCB exploration improve zero-shot performance on TextWorld cooking game families, reaching 72% and 68% of achievable points.