mlsanityData to 7 Oct 2026

Self-Driving Car Racing: Application of Deep Reinforcement Learning

arXiv 2410.22766 ↗First posted 30 Oct 2024cs.AIcited by 0

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

Abstract (arXiv)

This paper explores the application of deep reinforcement learning (RL) techniques in the domain of autonomous self-driving car racing. Motivated by the rise of AI-driven mobility and autonomous racing events, the project aims to develop an AI agent that efficiently drives a simulated car in the OpenAI Gymnasium CarRacing environment. We investigate various RL algorithms, including Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and novel adaptations that incorporate transfer learning and recurrent neural networks (RNNs) for enhanced performance. The project demonstrates that while DQN provides a strong baseline for policy learning, integrating ResNet and LSTM models significantly improves the agent's ability to capture complex spatial and temporal dynamics. PPO, particularly in continuous action spaces, shows promising results for fine control, although challenges such as policy collapse remain. We compare the performance of these approaches and outline future research directions focused on improving computational efficiency and addressing model stability. Our findings contribute to the ongoing development of AI systems in autonomous driving and related control tasks.

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 9 of its 16 references that are in the map. First posted on arXiv 30 Oct 2024.

Builds on

9 of 16 references.

References · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  3. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  4. 2019A Comprehensive Survey on Transfer Learningcited by 1,154
  5. 2015Prioritized Experience Replaycited by 1,070
  6. 2016Deep Exploration via Bootstrapped DQNcited by 518
  7. 2020Transfer Learning in Deep Reinforcement Learning: A Surveycited by 259
  8. 2019Recurrent Neural Networks (RNNs): A gentle Introduction and Overviewcited by 68
  9. 2022MAN: Multi-Action Networks Learningcited by 2

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  1. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
  2. 2020Deep Reinforcement Learning for Autonomous Driving: A Survey
  3. 2022Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning
  4. 2021Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning
  5. 2021Evaluating the Robustness of Deep Reinforcement Learning for Autonomous Policies in a Multi-agent Urban Driving Environment
  6. 2021Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture
  7. 2024Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning
  8. 2019Model-free Deep Reinforcement Learning for Urban Autonomous Driving