Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning

TL;DR

This paper enhances adversarial inverse reinforcement learning with semantic rewards to improve decision-making in complex, interactive autonomous driving environments, demonstrating superior performance over baselines in simulations.

Contribution

It introduces an augmented AIRL framework with semantic rewards and adapts it for complex autonomous driving scenarios, improving stability and performance.

Findings

Augmented AIRL outperforms baseline methods in simulations.

Performance of augmented AIRL is comparable to human experts.

Semantic rewards improve learning stability and decision quality.

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.