CDAN framework uses diversity exploration and adversarial self-correction for continual RL in continuous control, evaluated on new CAM environment with NSD metric showing 18.35% NSD improvement over baseline.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2019 2verdicts
UNVERDICTED 2representative citing papers
Classical agents outperform learning-based ones on MINOS and Stanford 3D Indoor Spaces, with learned agents weaker at collision avoidance and memory but stronger at handling ambiguity and noise.
citing papers explorer
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Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction
CDAN framework uses diversity exploration and adversarial self-correction for continual RL in continuous control, evaluated on new CAM environment with NSD metric showing 18.35% NSD improvement over baseline.
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To Learn or Not to Learn: Analyzing the Role of Learning for Navigation in Virtual Environments
Classical agents outperform learning-based ones on MINOS and Stanford 3D Indoor Spaces, with learned agents weaker at collision avoidance and memory but stronger at handling ambiguity and noise.