Unverified50% confidenceFactExact time
In ML-Agents, buffer_size corresponds to the total number of experience steps PPO collects between two policy updates, approximately equal to num_envs times time_horizon.
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50%
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Long-term
Relevance
10/13/2026
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Related Claims
VerifiedPPO was proposed by OpenAI in 2017 and limits the magnitude of policy updates through a clipping mechanism, truncating gradients when the probability ratio between the new and old policies exceeds the range of 1±0.2.64% similarVerifiedPPO通过引入裁剪目标函数将新旧策略概率比值限制在[1-ε, 1+ε]区间，替代TRPO的KL散度约束，简化了实现复杂度64% similarUnverifiedPPO constrains policy updates to a neighborhood of the old policy by clipping the importance sampling ratio, approximately achieving the stability guarantees of on-policy training.64% similarVerifiedThe core innovation of PPO lies in limiting the magnitude of policy updates through a clipped objective function, preventing training collapse caused by excessively large single updates.63% similarUnverifiedPPO's stability comes from the 'small-step update' constraint, whose mathematical form is jointly determined by the clip objective and the learning rate.63% similar
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