Augmentative Topology Agents For Open-ended Learning


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.

In Lifelong Learning of High-level Cognitive and Reasoning Skills Workshop @ IROS 2022
Muhammad Umair Nasir
Muhammad Umair Nasir

I love tackling challenges in Open-ended Learning and Jiu Jitsu.

Steven James
Steven James
Deputy Lab Director

My research interests include reinforcement learning and planning.

Christopher  Cleghorn
Christopher Cleghorn
Senior Applied Scientist