RLAD: Reinforcement Learning from Pixels for Autonomous Driving in Urban
  Environments
Daniel Coelho Miguel Oliveira Vitor Santos
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: i) an image encoder that leverages both
image augmentations and Adaptive Local Signal Mixing (A-LIX) layers; ii)
WayConv1D, which is a waypoint encoder that harnesses the 2D geometrical
information of the waypoints using 1D convolutions; and iii) 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.