Introduces the Agent State-Markov Policy Gradient (ASMPG) algorithm and a policy gradient theorem for non-Markovian decision processes by jointly optimizing agent state dynamics and control policy.
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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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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MIND uses sliced Wasserstein distance on Inception features to evaluate generative models, matching FID performance with 10x fewer samples and 100x faster computation while being more robust to moment-matching attacks.
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Policy Gradient Methods for Non-Markovian Reinforcement Learning
Introduces the Agent State-Markov Policy Gradient (ASMPG) algorithm and a policy gradient theorem for non-Markovian decision processes by jointly optimizing agent state dynamics and control policy.
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MIND: Monge Inception Distance for Generative Models Evaluation
MIND uses sliced Wasserstein distance on Inception features to evaluate generative models, matching FID performance with 10x fewer samples and 100x faster computation while being more robust to moment-matching attacks.