An RL agent learns to actively explore by being rewarded for inferring unobserved scene parts after short glimpse sequences, with sidekick policy learning enabling generalization to other active perception tasks.
Neural scene representation and rendering
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2019 2verdicts
UNVERDICTED 2representative citing papers
Multi-step predictive generative models form stable belief states capturing environment layout and agent pose, yielding higher data efficiency on RL tasks than model-free agents.
citing papers explorer
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Emergence of Exploratory Look-Around Behaviors through Active Observation Completion
An RL agent learns to actively explore by being rewarded for inferring unobserved scene parts after short glimpse sequences, with sidekick policy learning enabling generalization to other active perception tasks.
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Shaping Belief States with Generative Environment Models for RL
Multi-step predictive generative models form stable belief states capturing environment layout and agent pose, yielding higher data efficiency on RL tasks than model-free agents.