Unverified50% confidenceSolutionExact time
Common improvement approaches include curriculum learning (gradually increasing task difficulty) and domain randomization (randomizing physical parameters to improve policy robustness).
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10/28/2026
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VerifiedDomain Randomization forces a policy to learn robustness to environmental changes by randomizing simulation parameters during training.78% similarVerifiedDomain randomization technology randomly perturbs physical parameters (such as mass, friction coefficient, motor delay, etc.) during training, forcing the policy to learn to operate robustly under various conditions.76% similarUnverified策略梯度方法可以天然处理连续动作空间并能学习随机策略，这与基于价值的方法如Q-learning不同74% similarUnverifiedHaving activation function parameters participate in optimization may introduce new training instability factors, making the process more sensitive to learning rate and initialization.73% similarUnverifiedReinforcement learning in the post-training stage can improve answer accuracy on both in-distribution and out-of-distribution tasks, but does not consistently improve the validity of reasoning traces.73% similar
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