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Reward design for reinforcement learning agents can be difficult in situations where one not only wants the agent to achieve some effect in the world but where one also cares about how that effect is achieved.
A possibility for implementing curiosity and boredom in model-building neural controllers
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Automatic labeling of semantic roles
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Feudal networks for hierarchical reinforcement learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, and Koray Kavukcuoglu · 2017
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Playing text-adventure games with graph-based deep reinforcement learning
Prithviraj Ammanabrolu and Mark O Riedl · 2018
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Large-scale study of curiosity-driven learning
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Textworld: A learning environment for text-based games
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Data-efficient hierarchical reinforcement learning
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Visual reinforcement learning with imagined goals
Ashvin V Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Sahith Dambekodi, Spencer Frazier, Prithviraj Ammanabrolu, and Mark O Riedl · 2020
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Learning norms from stories: A prior for value aligned agents
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Content planning for neural story generation with aristotelian rescoring
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Deep reinforcement learning with stacked hierarchical attention for text-based games
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Training value-aligned reinforcement learning agents using a normative prior
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Scienceworld: Is your agent smarter than a 5th grader?, 2022
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