  Automated lane changing is a critical feature for advanced autonomous driving
systems. In recent years, reinforcement learning (RL) algorithms trained on
traffic simulators yielded successful results in computing lane changing
policies that strike a balance between safety, agility and compensating for
traffic uncertainty. However, many RL algorithms exhibit simulator bias and
policies trained on simple simulators do not generalize well to realistic
traffic scenarios. In this work, we develop a data driven traffic simulator by
training a generative adverserial network (GAN) on real life trajectory data.
The simulator generates randomized trajectories that resembles real life
traffic interactions between vehicles, which enables training the RL agent on
much richer and realistic scenarios. We demonstrate through simulations that RL
agents that are trained on GAN-based traffic simulator has stronger
generalization capabilities compared to RL agents trained on simple rule-driven
simulators.
