  Traditional planning and control methods could fail to find a feasible
trajectory for an autonomous vehicle to execute amongst dense traffic on roads.
This is because the obstacle-free volume in spacetime is very small in these
scenarios for the vehicle to drive through. However, that does not mean the
task is infeasible since human drivers are known to be able to drive amongst
dense traffic by leveraging the cooperativeness of other drivers to open a gap.
The traditional methods fail to take into account the fact that the actions
taken by an agent affect the behaviour of other vehicles on the road. In this
work, we rely on the ability of deep reinforcement learning to implicitly model
such interactions and learn a continuous control policy over the action space
of an autonomous vehicle. The application we consider requires our agent to
negotiate and open a gap in the road in order to successfully merge or change
lanes. Our policy learns to repeatedly probe into the target road lane while
trying to find a safe spot to move in to. We compare against two
model-predictive control-based algorithms and show that our policy outperforms
them in simulation.
