Despite the fast development of differentiable architecture search (DARTS),
it suffers from long-standing performance instability, which extremely limits
its application. Existing robustifying methods draw clues from the resulting
deteriorated behavior instead of finding out its causing factor. Various
indicators such as Hessian eigenvalues are proposed as a signal to stop
searching before the performance collapses. However, these indicator-based
methods tend to easily reject good architectures if the thresholds are
inappropriately set, let alone the searching is intrinsically noisy. In this
paper, we undertake a more subtle and direct approach to resolve the collapse.
We first demonstrate that skip connections have a clear advantage over other
candidate operations, where it can easily recover from a disadvantageous state
and become dominant. We conjecture that this privilege is causing degenerated
performance. Therefore, we propose to factor out this benefit with an auxiliary
skip connection, ensuring a fairer competition for all operations. We call this
approach DARTS-. Extensive experiments on various datasets verify that it can
substantially improve robustness. Our code is available at
this https URL .

本文针对不同 iable architecture search（DARTS）的长期性能不稳定问题，提出一种名为 DARTS - 的辅助 skip connection 的方法来解决它，从而提高其鲁棒性。

DARTS-: 无需指标强化的稳定性能提升

DARTS-: Robustly Stepping out of Performance Collapse Without Indicators

Proximal policy optimization (PPO) is one of the most successful deep
reinforcement-learning methods, achieving state-of-the-art performance across a
wide range of challenging tasks. However, its optimization behavior is still
far from being fully understood. In this paper, we show that PPO could neither
strictly restrict the likelihood ratio as it attempts to do nor enforce a
well-defined trust region constraint, which means that it may still suffer from
the risk of performance instability. To address this issue, we present an
enhanced PPO method, named Truly PPO. Two critical improvements are made in our
method: 1) it adopts a new clipping function to support a rollback behavior to
restrict the difference between the new policy and the old one; 2) the
triggering condition for clipping is replaced with a trust region-based one,
such that optimizing the resulted surrogate objective function provides
guaranteed monotonic improvement of the ultimate policy performance. It seems,
by adhering more truly to making the algorithm proximal - confining the policy
within the trust region, the new algorithm improves the original PPO on both
sample efficiency and performance.

本文介绍了一种名为 Truly PPO 的增强 PPO 方法，针对 PPO 在优化行为方面存在的问题进行了改进，通过使用新的剪辑函数来支持回滚行为，使用基于可信区域的触发条件替换剪辑的触发条件，从而提供了保证的拟态策略性能单调改进，从而改善了 PPO 在样本效率和性能方面的表现。