  The project's aim is to create an AI agent capable of selecting good actions
in a game-playing domain called Battlespace. Sequential domains like
Battlespace are important testbeds for planning problems, as such, the
Department of Defense uses such domains for wargaming exercises. The agents we
developed combine Monte Carlo Tree Search (MCTS) and Deep Q-Network (DQN)
techniques in an effort to navigate the game environment, avoid obstacles,
interact with adversaries, and capture the flag. This paper will focus on the
encoding techniques we explored to present complex structured data stored in a
Python class, a necessary precursor to an agent.
