  Autonomous Braking and Throttle control is key in developing safe driving
systems for the future. There exists a need for autonomous vehicles to
negotiate a multi-agent environment while ensuring safety and comfort. A Deep
Reinforcement Learning based autonomous throttle and braking system is
presented. For each time step, the proposed system makes a decision to apply
the brake or throttle. The throttle and brake are modelled as continuous action
space values. We demonstrate 2 scenarios where there is a need for a
sophisticated braking and throttle system, i.e when there is a static obstacle
in front of our agent like a car, stop sign. The second scenario consists of 2
vehicles approaching an intersection. The policies for brake and throttle
control are learned through computer simulation using Deep deterministic policy
gradients. The experiment shows that the system not only avoids a collision,
but also it ensures that there is smooth change in the values of throttle/brake
as it gets out of the emergency situation and abides by the speed regulations,
i.e the system resembles human driving.
