mlsanityData to 7 Oct 2026

WAD: A Deep Reinforcement Learning Agent for Urban Autonomous Driving

arXiv 2108.12134 ↗First posted 27 Aug 2021cs.AIcited by 0

On the map: End-to-end autonomous driving → Reinforcement learning for driving

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 22 of its 37 references that are in the map. First posted on arXiv 27 Aug 2021.

Builds on

22 of 37 references.

References · 22

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10 more
  1. 2015DeepDriving: Learning Affordance for Direct Perception in Autonomous Drivingcited by 229
  2. 2017Deep Reinforcement Learning framework for Autonomous Drivingcited by 224
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  1. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learning
  2. 2023Vision-based DRL Autonomous Driving Agent with Sim2Real Transfer
  3. 2023Review of Deep Reinforcement Learning for Autonomous Driving
  4. 2025A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator
  5. 2021Evaluating the Robustness of Deep Reinforcement Learning for Autonomous Policies in a Multi-agent Urban Driving Environment
  6. 2021Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learning
  7. 2021Affordance-based Reinforcement Learning for Urban Driving
  8. 2020Deep Reinforcement Learning for Autonomous Driving: A Survey