Transfer Dynamics in Emergent Evolutionary Curricula
- URL: http://arxiv.org/abs/2203.10941v1
- Date: Thu, 3 Mar 2022 21:10:22 GMT
- Title: Transfer Dynamics in Emergent Evolutionary Curricula
- Authors: Aaron Dharna, Amy K Hoover, Julian Togelius, L. B. Soros
- Abstract summary: PINSKY is a system for open-ended learning through neuroevolution in game-based domains.
This paper focuses on the role of transfer of policies from one evolutionary branch ("species") to another.
The most insightful finding is that inter-species transfer, while rare, is crucial to the system's success.
- Score: 4.692078300163222
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: PINSKY is a system for open-ended learning through neuroevolution in
game-based domains. It builds on the Paired Open-Ended Trailblazer (POET)
system, which originally explored learning and environment generation for
bipedal walkers, and adapts it to games in the General Video Game AI (GVGAI)
system. Previous work showed that by co-evolving levels and neural network
policies, levels could be found for which successful policies could not be
created via optimization alone. Studied in the realm of Artificial Life as a
potentially open-ended alternative to gradient-based fitness, minimal criteria
(MC)-based selection helps foster diversity in evolutionary populations. The
main question addressed by this paper is how the open-ended learning actually
works, focusing in particular on the role of transfer of policies from one
evolutionary branch ("species") to another. We analyze the dynamics of the
system through creating phylogenetic trees, analyzing evolutionary trajectories
of policies, and temporally breaking down transfers according to species type.
Furthermore, we analyze the impact of the minimal criterion on generated level
diversity and inter-species transfer. The most insightful finding is that
inter-species transfer, while rare, is crucial to the system's success.
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