Reinforcement learning commonly exhibits non-monotonic performance dynamics in single-task settings. During learning, a sequence of policies is generated whose true performance is unknown and may oscillate significantly across iterations. Despite this well-known phenomenon, standard experimental practice typically reports either the final visited policy or the best among periodically evaluated ones, implicitly assuming that these selection criteria reliably approximate the best-performing policy visited throughout the entire sequence. In this work, we present an empirical study on the implications of non-monotonicity for policy selection in single-task policy-based reinforcement learning. We introduce post-hoc diagnostic metrics to characterize performance degradation and assess the accuracy and efficiency of different selection criteria. These metrics allow us to evaluate commonly used selection strategies across standard continuous-control benchmarks using a well-established on-policy actor-critic method. Our results show that, in this representative experimental setting, widely adopted selection practices can deviate considerably from the best policy encountered during learning, leading to underperforming estimates and suboptimal use of computational resources. As an alternative, we recommend a validation-based policy selection strategy constrained by computational cost, which provides a more consistent and accurate approximation of the best visited policy while improving selection efficiency. Overall, our findings highlight policy selection as an underexplored factor in the evaluation protocols of reinforcement learning and motivate further study on explicit and standardized validation-based selection procedures.

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