Many games feature a progression of levels that doesn't adapt to the player.
This can be problematic because some players may get stuck if the progression
is too difficult, while others may find it boring if the progression is too
slow to get to more challenging levels. This can be addressed by building
levels based on the player's performance and preferences. In this work, we
formulate the problem of generating levels for a player as a Markov Decision
Process (MDP) and use adaptive dynamic programming (ADP) to solve the MDP
before assembling a level. We tested with two case studies and found that using
an ADP outperforms two baselines. Furthermore, we experimented with player
proxies and switched them in the middle of play, and we show that a simple
modification prior to running ADP results in quick adaptation. By using ADP,
which searches the entire MDP, we produce a dynamic progression of levels that
adapts to the player.

此论文使用自适应动态规划（ADP）来生成基于玩家表现和喜好的游戏关卡，通过在游戏过程中运行 ADP 进行简单修改使其能快速适应，实现动态关卡的生成。

基于马尔可夫决策过程的层级装配

Level Assembly as a Markov Decision Process

Variational autoencoders (VAEs) have been shown to be able to generate game
levels but require manual exploration of the learned latent space to generate
outputs with desired attributes. While conditional VAEs address this by
allowing generation to be conditioned on labels, such labels have to be
provided during training and thus require prior knowledge which may not always
be available. In this paper, we apply Gaussian Mixture VAEs (GMVAEs), a variant
of the VAE which imposes a mixture of Gaussians (GM) on the latent space,
unlike regular VAEs which impose a unimodal Gaussian. This allows GMVAEs to
cluster levels in an unsupervised manner using the components of the GM and
then generate new levels using the learned components. We demonstrate our
approach with levels from Super Mario Bros., Kid Icarus and Mega Man. Our
results show that the learned components discover and cluster level structures
and patterns and can be used to generate levels with desired characteristics.

本文介绍了应用高斯混合变分自编码器 (Gaussian Mixture VAEs) 生成游戏关卡的方法，该方法可以无需人工干预地将关卡按照特征进行聚类，并生成具有期望特征的新关卡。实验结果表明，学习到的关卡组成元素可以有效地发现与聚类关卡结构和模式。