  The Fighting Game AI Competition (FTGAIC) provides a challenging benchmark
for 2-player video game AI. The challenge arises from the large action space,
diverse styles of characters and abilities, and the real-time nature of the
game. In this paper, we propose a novel algorithm that combines Rolling Horizon
Evolution Algorithm (RHEA) with opponent model learning. The approach is
readily applicable to any 2-player video game. In contrast to conventional
RHEA, an opponent model is proposed and is optimized by supervised learning
with cross-entropy and reinforcement learning with policy gradient and
Q-learning respectively, based on history observations from opponent. The model
is learned during the live gameplay. With the learned opponent model, the
extended RHEA is able to make more realistic plans based on what the opponent
is likely to do. This tends to lead to better results. We compared our approach
directly with the bots from the FTGAIC 2018 competition, and found our method
to significantly outperform all of them, for all three character. Furthermore,
our proposed bot with the policy-gradient-based opponent model is the only one
without using Monte-Carlo Tree Search (MCTS) among top five bots in the 2019
competition in which it achieved second place, while using much less domain
knowledge than the winner.
