Paavo Parmas
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126
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ML×1AI×1
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2026
Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying
The paper introduces ReMax, a novel objective function that naturally encourages stochastic exploration in policy gradient reinforcement learning by evaluating expected maximum returns over multiple samples, and proposes RePPO for efficient optimization.
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