  Reinforcement learning (RL) offers a capable and intuitive structure for the
fundamental sequential decision-making problem. Despite impressive
breakthroughs, it can still be difficult to employ RL in practice in many
simple applications. In this paper, we try to address this issue by introducing
a method for designing the components of the RL environment for a given,
user-intended application. We provide an initial formalization for the problem
of RL component design, that concentrates on designing a good representation
for observation and action space. We propose a method named DeLF: Designing
Learning Environments with Foundation Models, that employs large language
models to design and codify the user's intended learning scenario. By testing
our method on four different learning environments, we demonstrate that DeLF
can obtain executable environment codes for the corresponding RL problems.
