In this work, we tackle the problem of open-ended learning by introducing a method that simultaneously evolves agents and increasingly challenging environments. Unlike previous open-ended approaches that optimize agents using a fixed neural network topology, we hypothesize that generalization can be improved by allowing agents' controllers to become more complex as they encounter more difficult environments. Our method, Augmentative Topology EPOET (ATEP), extends the Enhanced Paired Open-Ended Trailblazer (EPOET) algorithm by allowing agents to evolve their own neural network structures over time, adding complexity and capacity as necessary. Empirical results demonstrate that ATEP results in general agents capable of solving more environments than a fixed-topology baseline. We also investigate mechanisms for transferring agents between environments and find that a species-based approach further improves the performance and generalization of agents.

翻译：在本研究中，我们通过引入一种同时进化智能体和不断复杂化环境的方法，来解决开放式学习问题。不同于先前采用固定神经网络拓扑优化智能体的开放式方法，我们假设当智能体的控制器随着遇到更困难环境而变得更加复杂时，其泛化能力能够得到提升。我们的方法——增强拓扑EPOET（ATEP）——扩展了增强型配对开放式开拓者（EPOET）算法，允许智能体随时间进化自身的神经网络结构，在必要时增加复杂性和容量。实证结果表明，ATEP能够产生比固定拓扑基线更能解决更多环境的通用智能体。我们还研究了智能体在环境间迁移的机制，发现基于物种的方法进一步提升了智能体的性能和泛化能力。