Точка Синхронізації

Researchers specializing in artificial intelligence published a technical paper on the arXiv preprint server on February 10, 2025, detailing a significant instability issue in Proximal Policy Optimization (PPO) caused by mode-dependent architectural components. The study highlights how common machine learning layers, such as Batch Normalization and dropout, exhibit different behaviors during training versus evaluation, leading to catastrophic reward collapse and distributional drift in visual reinforcement learning tasks. This technical breakdown occurs because the discrepancy between the training and inference modes creates a policy mismatch that undermines the reliability of on-policy optimization algorithms. The core of the problem lies in the transition between the model's active learning phase and its deployment or evaluation phase. In traditional deep learning, Batch Normalization is used to stabilize training by normalizing inputs according to mini-batch statistics. However, in the context of reinforcement learning (RL), where the agent must learn from its own evolving interactions with an environment, these shifts in statistics become more volatile. The researchers found that when the algorithm expects one distribution during training but encounters another during evaluation, the agent's policy becomes inconsistent, effectively breaking the feedback loop required for steady improvement. To address this vulnerability, the paper introduces a methodology referred to as mode-dependent rectification. This approach aims to harmonize the behavior of these architectural components across both learning and testing phases to ensure that the policy remains stable. By mitigating the 'mismatch' between how a neural network perceives data during optimization and how it acts upon it during live runs, the researchers claim that PPO can achieve more robust results, particularly in complex visual environments where these errors are most prevalent. Beyond just identifying the flaws in Batch Normalization, the research serves as a critical guide for developers building high-stakes autonomous systems. The findings suggest that many of the standard 'best practices' from computer vision cannot be applied to reinforcement learning without significant adjustments. As the AI industry moves toward more sophisticated visual agents, understanding these underlying synchronization issues becomes essential for preventing sudden performance degradation and ensuring that trained models perform as expected in real-world scenarios.