  Many current behavior generation methods struggle to handle real-world
traffic situations as they do not scale well with complexity. However,
behaviors can be learned off-line using data-driven approaches. Especially,
reinforcement learning is promising as it implicitly learns how to behave
utilizing collected experiences. In this work, we combine policy-based
reinforcement learning with local optimization to foster and synthesize the
best of the two methodologies. The policy-based reinforcement learning
algorithm provides an initial solution and guiding reference for the
post-optimization. Therefore, the optimizer only has to compute a single
homotopy class, e.g.\ drive behind or in front of the other vehicle. By storing
the state-history during reinforcement learning, it can be used for constraint
checking and the optimizer can account for interactions. The post-optimization
additionally acts as a safety-layer and the novel method, thus, can be applied
in safety-critical applications. We evaluate the proposed method using
lane-change scenarios with a varying number of vehicles.
