Uncertainty-aware RL framework using ensemble disagreement and annotation variability reduces reward-hacking trap visits by 93.7% across grid and continuous control tasks while remaining robust to 30% label noise.
Conservative Q-learning for offline reinforcement learning
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
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Delightful Policy Gradient gates updates with advantage times surprisal to suppress rare failures while preserving rare successes in distributed RL with stale or buggy data.
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Uncertainty-Aware Reward Discounting for Mitigating Reward Hacking
Uncertainty-aware RL framework using ensemble disagreement and annotation variability reduces reward-hacking trap visits by 93.7% across grid and continuous control tasks while remaining robust to 30% label noise.
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Delightful Distributed Policy Gradient
Delightful Policy Gradient gates updates with advantage times surprisal to suppress rare failures while preserving rare successes in distributed RL with stale or buggy data.