Require: , the fixed number of removal γ t , γ v 1: Load parameters θ from pre-trained policy network 2: Initialize s * as the all-zero vector with the same dimension of s j 3: Randomly sample a j ∼ π(a| s j ; θ); compute p j = π(a = 0| s j ; θ) 7: if a j == 0 then Rank T based on p j from high to low, obtain T rank 12: end for 15:  19 : 22: end for the ability to distinguish the obvious false positive instances, which means the discrimination of the indistinguishable wrong-labeled instances are still ambiguous.
this indistinguishable part is the criterion to reflect the quality of the agent.