MCTS-based algorithms, such as MuZero and its derivatives, have achieved
widespread success in various decision-making domains. These algorithms employ
the reanalyze process to enhance sample efficiency, albeit at the expense of
significant wall-clock time consumption. To address this issue, we propose a
general approach named ReZero to boost MCTS-based algorithms. Specifically, we
propose a new scheme that simplifies data collecting and reanalyzing, which
significantly reduces the search cost while guarantees the performance as well.
Furthermore, to accelerate each search process, we conceive a method to reuse
the subsequent information in the trajectory. The corresponding analysis
conducted on the bandit model also provides auxiliary theoretical
substantiation for our design. Experiments conducted on Atari environments and
board games demonstrates that ReZero substantially improves training speed
while maintaining high sample efficiency. The code is available as part of the
LightZero benchmark at this https URL

提供了一种名为 ReZero 的通用方法来提高基于 MCTS 的算法，在显著降低搜索成本的同时保证性能，从而大幅提高训练速度和样本效率。

ReZero: 提高基于 MCTS 算法的效率通过及时和快速的重新分析

ReZero: Boosting MCTS-based Algorithms by Just-in-Time and Speedy  Reanalyze

Neural architecture search (NAS) aims to discover network architectures with
desired properties such as high accuracy or low latency. Recently,
differentiable NAS (DNAS) has demonstrated promising results while maintaining
a search cost orders of magnitude lower than reinforcement learning (RL) based
NAS. However, DNAS models can only optimize differentiable loss functions in
search, and they require an accurate differentiable approximation of
non-differentiable criteria. In this work, we present UNAS, a unified framework
for NAS, that encapsulates recent DNAS and RL-based approaches under one
framework. Our framework brings the best of both worlds, and it enables us to
search for architectures with both differentiable and non-differentiable
criteria in one unified framework while maintaining a low search cost. Further,
we introduce a new objective function for search based on the generalization
gap that prevents the selection of architectures prone to overfitting. We
present extensive experiments on the CIFAR-10, CIFAR-100, and ImageNet datasets
and we perform search in two fundamentally different search spaces. We show
that UNAS obtains the state-of-the-art average accuracy on all three datasets
when compared to the architectures searched in the DARTS space. Moreover, we
show that UNAS can find an efficient and accurate architecture in the
ProxylessNAS search space, that outperforms existing MobileNetV2 based
architectures. The source code is available at this https URL .

UNAS 是一个统一的框架，将最新的 DNAS 和 RL 方法封装在一个框架中，在维持较低的搜索成本的同时，能够在一个统一的框架中搜索具有可微和不可微标准的架构。通过 CIFAR-10、CIFAR-100 和 ImageNet 数据集的广泛实验，我们展示了 UNAS 在所有三个数据集上的平均精度与在 DARTS 空间中搜索的架构相比具有最先进的表现，并且在 ProxylessNAS 搜索空间中可以找到一个高效而准确的架构，超越了现有的基于 MobileNetV2 的架构。