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Found 2 papers, 2 papers with code
Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision Processes
Many real-world problems that require making optimal sequences of decisions under uncertainty involve costs when the agent wishes to obtain information about its environment.
Batch Exploration with Examples for Scalable Robotic Reinforcement Learning
Concretely, we propose an exploration technique, Batch Exploration with Examples (BEE), that explores relevant regions of the state-space, guided by a modest number of human provided images of important states.
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