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A synthesis of automated planning and reinforcement learning for efficient, robust decision-making
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Outgoing claims: the papers this work cites, builds on, or argues with.
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The first learning track of the international planning competition
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Planning under time constraints in stochastic domains
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Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming
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The actorʼs view of automated planning and acting: A position paper
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Execution monitoring in robotics: A survey
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Relational Reinforcement Learning
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Recent Advances in Hierarchical Reinforcement Learning
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Plan-based reward shaping for reinforcement learning
inferredcites →https://doi.org/10.1109/tnn.1998.712192openalex-snapshot
Reinforcement Learning: An Introduction
inferredcites →https://doi.org/10.1109/tnn.2004.842673openalex-snapshot
Reinforcement Learning: An Introduction
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Scaling Up Reinforcement Learning through Targeted Exploration
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Teleo-Reactive Programs for Agent Control
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Potassco: The Potsdam Answer Set Solving Collection
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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