  The kind of closed-loop verification likely to be required for autonomous
vehicle (AV) safety testing is beyond the reach of traditional test
methodologies and discrete verification. Validation puts the autonomous vehicle
system to the test in scenarios or situations that the system would likely
encounter in everyday driving after its release. These scenarios can either be
controlled directly in a physical (closed-course proving ground) or virtual
(simulation of predefined scenarios) environment, or they can arise
spontaneously during operation in the real world (open-road testing or
simulation of randomly generated scenarios).
  In AV testing, simulation serves primarily two purposes: to assist the
development of a robust autonomous vehicle and to test and validate the AV
before release. A challenge arises from the sheer number of scenario variations
that can be constructed from each of the above sources due to the high number
of variables involved (most of which are continuous). Even with continuous
variables discretized, the possible number of combinations becomes practically
infeasible to test. To overcome this challenge we propose using reinforcement
learning (RL) to generate failure examples and unexpected traffic situations
for the AV software implementation. Although reinforcement learning algorithms
have achieved notable results in games and some robotic manipulations, this
technique has not been widely scaled up to the more challenging real world
applications like autonomous driving.
