  This paper describe an hybrid agent trained to play in Fantasy Football AI
which participated in the Bot Bowl III competition. The agent, MimicBot, is
implemented using a specifically designed deep policy network and trained using
a combination of imitation and reinforcement learning. Previous attempts in
using a reinforcement learning approach in such context failed for a number of
reasons, e.g. due to the intrinsic randomness in the environment and the large
and uneven number of actions available, with a curriculum learning approach
failing to consistently beat a randomly paying agent. Currently no machine
learning approach can beat a scripted bot which makes use of the domain
knowledge on the game. Our solution, thanks to an imitation learning and a
hybrid decision-making process, consistently beat such scripted agents.
Moreover we shed lights on how to more efficiently train in a reinforcement
learning setting while drastically increasing sample efficiency. MimicBot is
the winner of the Bot Bowl III competition, and it is currently the
state-of-the-art solution.
