  Creating systems capable of generating virtually infinite variations of
complex and novel behaviour without predetermined goals or limits is a major
challenge in the field of AI. This challenge has been addressed through the
development of several open-ended algorithms that can continuously generate new
and diverse behaviours, such as the POET and Enhanced-POET algorithms for
co-evolving environments and agent behaviour. One of the challenges with
existing methods however, is that they struggle to continuously generate
complex environments. In this work, we propose LLM-POET, a modification of the
POET algorithm where the environment is both created and mutated using a Large
Language Model (LLM). By fine-tuning a LLM with text representations of
Evolution Gym environments and captions that describe the environment, we were
able to generate complex and diverse environments using natural language. We
found that not only could the LLM produce a diverse range of environments, but
compared to the CPPNs used in Enhanced-POET for environment generation, the LLM
allowed for a 34% increase in the performance gain of co-evolution. This
increased performance suggests that the agents were able to learn a more
diverse set of skills by training on more complex environments.
