WAD: A Deep Reinforcement Learning Agent for Urban Autonomous Driving

• URL: http://arxiv.org/abs/2108.12134v1
• Date: Fri, 27 Aug 2021 06:48:31 GMT
• Title: WAD: A Deep Reinforcement Learning Agent for Urban Autonomous Driving
• Authors: Arjit Sharma and Sahil Sharma
• Abstract summary: 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. 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.
• Score: 8.401473551081747
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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