  Multiagent systems appear in most social, economical, and political
situations. In the present work we extend the Deep Q-Learning Network
architecture proposed by Google DeepMind to multiagent environments and
investigate how two agents controlled by independent Deep Q-Networks interact
in the classic videogame Pong. By manipulating the classical rewarding scheme
of Pong we demonstrate how competitive and collaborative behaviors emerge.
Competitive agents learn to play and score efficiently. Agents trained under
collaborative rewarding schemes find an optimal strategy to keep the ball in
the game as long as possible. We also describe the progression from competitive
to collaborative behavior. The present work demonstrates that Deep Q-Networks
can become a practical tool for studying the decentralized learning of
multiagent systems living in highly complex environments.
