  This paper deploys the Deep Deterministic Policy Gradient (DDPG) algorithm
for longitudinal and lateral control of a simulated car to solve a path
following task. The DDPG agent was implemented using PyTorch and trained and
evaluated on a custom kinematic bicycle environment created in Python. The
performance was evaluated by measuring cross-track error and velocity error,
relative to a reference path. Results show how the agent can learn a policy
allowing for small cross-track error, as well as adapting the acceleration to
minimize the velocity error.
