Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning
Pin Wang Dapeng Liu Jiayu Chen Hanhan Li Ching-Yao Chan Pin Wang Dapeng Liu Jiayu Chen Hanhan Li Ching-Yao Chan
Making decisions in complex driving environments is a challenging task for autonomous agents. Imitation learning methods have great potentials for achieving such a goal. Adversarial Inverse Reinforcement Learning (AIRL) is one of the state-of-art imitation learning methods that can learn both a behavioral policy and a reward function simultaneously, yet it is only demonstrated in simple and static...