Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning

Abstract

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.

arXiv commentThe 2021 International Conference on Robotics and Automation (ICRA 2021)

References in corpus (8)

• Continuous control with deep reinforcement learning
• Congested Traffic States in Empirical Observations and Microscopic Simulations
• Trust Region Policy Optimization
• Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
• Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving
• A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models
• Wasserstein Adversarial Imitation Learning
• Automated Driving Maneuvers under Interactive Environment based on Deep Reinforcement Learning

Cited by in corpus (1)

• Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks