All by Myself: Learning Individualized Competitive Behaviour with a
  Contrastive Reinforcement Learning optimization
Pablo Barros Alessandra Sciutti
In a competitive game scenario, a set of agents have to learn decisions that
maximize their goals and minimize their adversaries' goals at the same time.
Besides dealing with the increased dynamics of the scenarios due to the
opponents' actions, they usually have to understand how to overcome the
opponent's strategies. Most of the common solutions, usually based on continual
learning or centralized multi-agent experiences, however, do not allow the
development of personalized strategies to face individual opponents. In this
paper, we propose a novel model composed of three neural layers that learn a
representation of a competitive game, learn how to map the strategy of specific
opponents, and how to disrupt them. The entire model is trained online, using a
composed loss based on a contrastive optimization, to learn competitive and
multiplayer games. We evaluate our model on a pokemon duel scenario and the
four-player competitive Chef's Hat card game. Our experiments demonstrate that
our model achieves better performance when playing against offline, online, and
competitive-specific models, in particular when playing against the same
opponent multiple times. We also present a discussion on the impact of our
model, in particular on how well it deals with on specific strategy learning
for each of the two scenarios.