  There are relatively few conventions followed in reinforcement learning (RL)
environments to structure the action spaces. As a consequence the application
of RL algorithms to tasks with large action spaces with multiple components
require additional effort to adjust to different formats. In this paper we
introduce {\em Conditional Action Trees} with two main objectives: (1) as a
method of structuring action spaces in RL to generalise across several action
space specifications, and (2) to formalise a process to significantly reduce
the action space by decomposing it into multiple sub-spaces, favoring a
multi-staged decision making approach. We show several proof-of-concept
experiments validating our scheme, ranging from environments with basic
discrete action spaces to those with large combinatorial action spaces commonly
found in RTS-style games.
