GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving
This work addresses sample efficiency and stability issues in reinforcement learning for autonomous driving and robotics, representing a strong specific gain rather than a foundational breakthrough.
The paper tackles the high sample complexity and instability of deep reinforcement learning by proposing General Reinforced Imitation (GRI), a method that integrates expert demonstrations with exploration, resulting in a 17% performance improvement over the previous state-of-the-art on the CARLA Leaderboard for vision-based autonomous driving.
Deep reinforcement learning (DRL) has been demonstrated to be effective for several complex decision-making applications such as autonomous driving and robotics. However, DRL is notoriously limited by its high sample complexity and its lack of stability. Prior knowledge, e.g. as expert demonstrations, is often available but challenging to leverage to mitigate these issues. In this paper, we propose General Reinforced Imitation (GRI), a novel method which combines benefits from exploration and expert data and is straightforward to implement over any off-policy RL algorithm. We make one simplifying hypothesis: expert demonstrations can be seen as perfect data whose underlying policy gets a constant high reward. Based on this assumption, GRI introduces the notion of offline demonstration agents. This agent sends expert data which are processed both concurrently and indistinguishably with the experiences coming from the online RL exploration agent. We show that our approach enables major improvements on vision-based autonomous driving in urban environments. We further validate the GRI method on Mujoco continuous control tasks with different off-policy RL algorithms. Our method ranked first on the CARLA Leaderboard and outperforms World on Rails, the previous state-of-the-art, by 17%.