  Autonomous navigation in crowded, complex urban environments requires
interacting with other agents on the road. A common solution to this problem is
to use a prediction model to guess the likely future actions of other agents.
While this is reasonable, it leads to overly conservative plans because it does
not explicitly model the mutual influence of the actions of interacting agents.
This paper builds a reinforcement learning-based method named MIDAS where an
ego-agent learns to affect the control actions of other cars in urban driving
scenarios. MIDAS uses an attention-mechanism to handle an arbitrary number of
other agents and includes a "driver-type" parameter to learn a single policy
that works across different planning objectives. We build a simulation
environment that enables diverse interaction experiments with a large number of
agents and methods for quantitatively studying the safety, efficiency, and
interaction among vehicles. MIDAS is validated using extensive experiments and
we show that it (i) can work across different road geometries, (ii) results in
an adaptive ego policy that can be tuned easily to satisfy performance criteria
such as aggressive or cautious driving, (iii) is robust to changes in the
driving policies of external agents, and (iv) is more efficient and safer than
existing approaches to interaction-aware decision-making.
