Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning
This work addresses the problem of improving autonomous driving decision-making for safer and more efficient navigation, though it is incremental as it builds on existing AIRL methods.
The paper tackles the challenge of decision-making for autonomous driving in interactive environments by augmenting Adversarial Inverse Reinforcement Learning (AIRL) with semantic rewards, resulting in performance that outperforms baselines and is comparable to experts across four metrics.
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 environments where no interactions are introduced. In this paper, we improve and stabilize AIRL's performance by augmenting it with semantic rewards in the learning framework. Additionally, we adapt the augmented AIRL to a more practical and challenging decision-making task in a highly interactive environment in autonomous driving. The proposed method is compared with four baselines and evaluated by four performance metrics. Simulation results show that the augmented AIRL outperforms all the baseline methods, and its performance is comparable with that of the experts on all of the four metrics.