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We focus on creating agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games -- environments wherein an agent perceives and interacts with a world through natural language.
NAIL: A general interactive fiction agent
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Deep reinforcement learning with a natural language action space
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Constrained policy optimization
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Agent Foundations for Aligning Machine Intelligence with Human Interests: A Technical Research Agenda , pages 103–125. Machine Intelligence Research Institute (MIRI) technical report, Springer Berlin Heidelberg
Nate Soares and Benya Fallenstein. 2017 · 2017
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Textworld: A learning environment for text-based games
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Learning norms from stories: A prior for value aligned agents
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Learning human objectives by evaluating hypothetical behavior
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Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J Mankowitz, and Shie Mannor. 2018 · 2018
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Language Models are Unsupervised Multitask Learners
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Reward constrained policy optimization
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Learning dynamic belief graphs to generalize on text-based games
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Graph Constrained Reinforcement Learning for Natural Language Action Spaces
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Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines
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Reward reports for reinforcement learning
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