  Learning in multi-agent environments is difficult due to the non-stationarity
introduced by an opponent's or partner's changing behaviors. Instead of
reactively adapting to the other agent's (opponent or partner) behavior, we
propose an algorithm to proactively influence the other agent's strategy to
stabilize -- which can restrain the non-stationarity caused by the other agent.
We learn a low-dimensional latent representation of the other agent's strategy
and the dynamics of how the latent strategy evolves with respect to our robot's
behavior. With this learned dynamics model, we can define an unsupervised
stability reward to train our robot to deliberately influence the other agent
to stabilize towards a single strategy. We demonstrate the effectiveness of
stabilizing in improving efficiency of maximizing the task reward in a variety
of simulated environments, including autonomous driving, emergent
communication, and robotic manipulation. We show qualitative results on our
website: https://sites.google.com/view/stable-marl/.
