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.

本文提出了一种名为ATEP的增强型拓扑EPOET算法来同时进化越来越具有挑战性的环境和智能体的控制器结构，并证明这种方法相比于固定神经网络结构的基线算法具有更强的泛化性能，同时采用基于物种的转移机制有利于进一步提升智能体的表现和泛化能力。

开放式学习的增强拓扑智能体