In the context of neuroevolution, Quality-Diversity algorithms have proven effective in generating repertoires of diverse and efficient policies by relying on the definition of a behavior space. A natural goal induced by the creation of such a repertoire is trying to achieve behaviors on demand, which can be done by running the corresponding policy from the repertoire. However, in uncertain environments, two problems arise. First, policies can lack robustness and repeatability, meaning that multiple episodes under slightly different conditions often result in very different behaviors. Second, due to the discrete nature of the repertoire, solutions vary discontinuously. Here we present a new approach to achieve behavior-conditioned trajectory generation based on two mechanisms: First, MAP-Elites Low-Spread (ME-LS), which constrains the selection of solutions to those that are the most consistent in the behavior space. Second, the Quality-Diversity Transformer (QDT), a Transformer-based model conditioned on continuous behavior descriptors, which trains on a dataset generated by policies from a ME-LS repertoire and learns to autoregressively generate sequences of actions that achieve target behaviors. Results show that ME-LS produces consistent and robust policies, and that its combination with the QDT yields a single policy capable of achieving diverse behaviors on demand with high accuracy.

翻译：在神经进化背景下，质量多样性算法通过依赖行为空间的定义，在生成多样化且高效策略的库方面已证明其有效性。此类库的创建自然引出一个目标：试图按需实现多种行为，这可通过执行库中对应的策略来完成。然而，在不确定环境中会出现两个问题。首先，策略可能缺乏鲁棒性和可重复性，这意味着在略有不同的条件下多次执行往往导致截然不同的行为。其次，由于库的离散性质，解的分布呈非连续性。本文提出一种基于两种机制实现行为条件轨迹生成的新方法：第一，MAP-Elites低分散算法，该算法将解的筛选范围限制在行为空间中最具一致性的那些；第二，质量多样性变换器，这是一种基于连续行为描述符的变换器模型，它利用ME-LS库中策略生成的数据集进行训练，并学习自回归地生成能够达成目标行为的动作序列。结果表明，ME-LS能够生成一致且鲁棒的策略，其与QDT的结合可产生一种单一策略，能够以高精度按需实现多样化的行为。