Automated machine learning (AutoML) systems commonly ensemble models post hoc
to improve predictive performance, typically via greedy ensemble selection
(GES). However, we believe that GES may not always be optimal, as it performs a
simple deterministic greedy search. In this work, we introduce two novel
population-based ensemble selection methods, QO-ES and QDO-ES, and compare them
to GES. While QO-ES optimises solely for predictive performance, QDO-ES also
considers the diversity of ensembles within the population, maintaining a
diverse set of well-performing ensembles during optimisation based on ideas of
quality diversity optimisation. The methods are evaluated using 71
classification datasets from the AutoML benchmark, demonstrating that QO-ES and
QDO-ES often outrank GES, albeit only statistically significant on validation
data. Our results further suggest that diversity can be beneficial for post hoc
ensembling but also increases the risk of overfitting.

本文介绍了两种基于种群的集成选择方法 QO-ES 和 QDO-ES，与现有的贪心集成选择方法 GES 进行比较，结果表明 QO-ES 和 QDO-ES 可以有效提高预测性能，但多样性也会增加过拟合的风险。

Q (D) O-ES: 基于人口的自动机器学习中后验高斯集成选择的质量 (多样性) 优化

Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc  Ensemble Selection in AutoML

Recent progress in reinforcement learning (RL) has started producing
generally capable agents that can solve a distribution of complex environments.
These agents are typically tested on fixed, human-authored environments. On the
other hand, quality diversity (QD) optimization has been proven to be an
effective component of environment generation algorithms, which can generate
collections of high-quality environments that are diverse in the resulting
agent behaviors. However, these algorithms require potentially expensive
simulations of agents on newly generated environments. We propose Deep
Surrogate Assisted Generation of Environments (DSAGE), a sample-efficient QD
environment generation algorithm that maintains a deep surrogate model for
predicting agent behaviors in new environments. Results in two benchmark
domains show that DSAGE significantly outperforms existing QD environment
generation algorithms in discovering collections of environments that elicit
diverse behaviors of a state-of-the-art RL agent and a planning agent. Our
source code and videos are available at this https URL.

本论文介绍了一种深度代理辅助环境生成技术（DSAGE）, 该技术可以有效生成高质量、多样化的环境，以探索最先进的强化学习和规划代理的多样行为。

基于深度代理的环境生成

Deep Surrogate Assisted Generation of Environments

Consider the problem of training robustly capable agents. One approach is to
generate a diverse collection of agent polices. Training can then be viewed as
a quality diversity (QD) optimization problem, where we search for a collection
of performant policies that are diverse with respect to quantified behavior.
Recent work shows that differentiable quality diversity (DQD) algorithms
greatly accelerate QD optimization when exact gradients are available. However,
agent policies typically assume that the environment is not differentiable. To
apply DQD algorithms to training agent policies, we must approximate gradients
for performance and behavior. We propose two variants of the current
state-of-the-art DQD algorithm that compute gradients via approximation methods
common in reinforcement learning (RL). We evaluate our approach on four
simulated locomotion tasks. One variant achieves results comparable to the
current state-of-the-art in combining QD and RL, while the other performs
comparably in two locomotion tasks. These results provide insight into the
limitations of current DQD algorithms in domains where gradients must be
approximated. Source code is available at this https URL

探讨在训练鲁棒性强的机器人智能体中，使用多样化的代理策略以提高质量多样性优化问题的效率。通过两种导数近似方法实现多样性质量优化算法，并使用四种机器人仿真测试其性能，揭示当前算法在需要近似梯度的领域中的局限性。