Balancing Two-Player Stochastic Games with Soft Q-Learning
  Within the context of video games the notion of perfectly rational agents can
be undesirable as it leads to uninteresting situations, where humans face tough
adversarial decision makers. Current frameworks for stochastic games and
reinforcement learning prohibit tuneable strategies as they seek optimal
performance. In this paper, we enable such tuneable behaviour by generalising
soft Q-learning to stochastic games, where more than one agent interact
strategically. We contribute both theoretically and empirically. On the theory
side, we show that games with soft Q-learning exhibit a unique value and
generalise team games and zero-sum games far beyond these two extremes to cover
a continuous spectrum of gaming behaviour. Experimentally, we show how tuning
agents' constraints affect performance and demonstrate, through a neural
network architecture, how to reliably balance games with high-dimensional
representations.
