Constraining Model-Free Reinforcement Learning Algorithms with an Application to Autonomous Driving

Development of deep, model-free reinforcement learning methods which allow to impose both hard and soft constraints on the agent's behaviour. These algorithms are analyzed and used to train virtual drivers for highly-automated and autonomous vehicles on highways, where the enforcement of hard safety constraints and soft comfort constraints ensures a reliable driving agent is learned.