Deep reinforcement learning (RL) provides powerful methods for training
optimal sequential decision-making agents. As collecting real-world
interactions can entail additional costs and safety risks, the common paradigm
of sim2real conducts training in a simulator, followed by real-world
deployment. Unfortunately, RL agents easily overfit to the choice of simulated
training environments, and worse still, learning ends when the agent masters
the specific set of simulated environments. In contrast, the real world is
highly open-ended, featuring endlessly evolving environments and challenges,
making such RL approaches unsuitable. Simply randomizing over simulated
environments is insufficient, as it requires making arbitrary distributional
assumptions and can be combinatorially less likely to sample specific
environment instances that are useful for learning. An ideal learning process
should automatically adapt the training environment to maximize the learning
potential of the agent over an open-ended task space that matches or surpasses
the complexity of the real world. This thesis develops a class of methods
called Unsupervised Environment Design (UED), which aim to produce such
open-ended processes. Given an environment design space, UED automatically
generates an infinite sequence or curriculum of training environments at the
frontier of the learning agent's capabilities. Through extensive empirical
studies and theoretical arguments founded on minimax-regret decision theory and
game theory, the findings in this thesis show that UED autocurricula can
produce RL agents exhibiting significantly improved robustness and
generalization to previously unseen environment instances. Such autocurricula
are promising paths toward open-ended learning systems that achieve more
general intelligence by continually generating and mastering additional
challenges of their own design.

该论文介绍了一种称为无监督环境设计（UED）的方法，通过自动生成无限的训练环境序列或课程以匹配或超过真实世界的复杂性，从而实现深度强化学习代理在鲜有环境示例中表现出显著改进的鲁棒性和泛化能力，这些自生成的环境课程为不断生成和掌握自主设计的额外挑战的开放式学习系统提供了有希望的路径。