  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.
