WAD: A Deep Reinforcement Learning Agent for Urban Autonomous Driving
  Urban autonomous driving is an open and challenging problem to solve as the
decision-making system has to account for several dynamic factors like
multi-agent interactions, diverse scene perceptions, complex road geometries,
and other rarely occurring real-world events. On the other side, with deep
reinforcement learning (DRL) techniques, agents have learned many complex
policies. They have even achieved super-human-level performances in various
Atari Games and Deepmind's AlphaGo. However, current DRL techniques do not
generalize well on complex urban driving scenarios. This paper introduces the
DRL driven Watch and Drive (WAD) agent for end-to-end urban autonomous driving.
Motivated by recent advancements, the study aims to detect important
objects/states in high dimensional spaces of CARLA and extract the latent state
from them. Further, passing on the latent state information to WAD agents based
on TD3 and SAC methods to learn the optimal driving policy. Our novel approach
utilizing fewer resources, step-by-step learning of different driving tasks,
hard episode termination policy, and reward mechanism has led our agents to
achieve a 100% success rate on all driving tasks in the original CARLA
benchmark and set a new record of 82% on further complex NoCrash benchmark,
outperforming the state-of-the-art model by more than +30% on NoCrash
benchmark.
