Molecule Builder: Environment for Testing Reinforcement Learning Agents

Petr Hyner, Jan Hula, Mikolás Janota

Abstract: We present a reinforcement learning environment designed to test agents’ ability to solve problems that can be naturally decomposed using subgoals. This environment is built on top of the PyVGDL game engine and enables to generate problem instances by specifying the dependency structure of subgoals. Its purpose is to enable faster development of Reinforcement Learning algorithms that solve problems by proposing subgoals and then reaching these subgoals.