RLAD: Reinforcement Learning From Pixels for Autonomous Driving in Urban Environments

Current approaches of Reinforcement Learning (RL) applied in urban Autonomous Driving (AD) focus on decoupling the perception training from the driving policy training. The main reason is to avoid training a convolution encoder alongside a policy network, which is known to have issues related to sample efficiency, degenerated feature representations, and catastrophic self-overfitting. However, this paradigm can lead to representations of the environment that are not aligned with the downstream task, which may result in suboptimal performances. To address this limitation, this paper proposes RLAD, the first Reinforcement Learning from Pixels (RLfP) method applied in the urban AD domain. We propose several techniques to enhance the performance of an RLfP algorithm in this domain, including: 1) an image encoder that leverages both image augmentations and Adaptive Local Signal Mixing (A-LIX) layers; 2) WayConv1D, which is a waypoint encoder that harnesses the 2D geometrical information of the waypoints using 1D convolutions; and 3) an auxiliary loss to increase the significance of the traffic lights in the latent representation of the environment. Experimental results show that RLAD significantly outperforms all state-of-the-art RLfP methods on the NoCrash benchmark. We also present an infraction analysis on the NoCrash-regular benchmark, which indicates that RLAD performs better than all other methods in terms of both collision rate and red light infractions. The source code of RLAD is available at
https://github.com/DanielCoelho112/rlad
. Note to Practitioners—The practical problem that motivated our work is the application of RLfP in urban AD. Unlike common approaches in AD, our main goal is to learn intermediate feature representations of the environment that are aligned with the driving task. Our method is the first RLfP algorithm applied in the urban AD domain, and results show that it surpasses all state-of-the-art RLfP methods on the NoCrash benchmark. The key insights and contributions of our work lie in the development of effective techniques for RLfP in urban AD, improving the overall performance and safety of AD systems. Beyond AD, the techniques and insights from our work could be extended to other domains that involve RL from pixel inputs, such as robotics, and video game agents. These applications would benefit from the enhanced performance offered by our proposed methods.