用深度强化学习训练赛车AI，提升复杂动态下的驾驶能力。
Self-Driving Car Racing: Application of Deep Reinforcement Learning
- 结合ResNet与LSTM增强时空特征捕捉能力
- PPO在连续动作空间表现优异但存在策略坍塌问题
- 适合关注自动驾驶控制与强化学习应用的研究者
本文探索深度强化学习技术在自动驾驶赛车领域的应用。基于人工智能驱动出行和自主赛车赛事的发展，研究旨在开发一个高效驾驶模拟赛车的AI智能体，环境为OpenAI Gymnasium CarRacing。实验对比了Deep Q-Network（DQN）、Proximal Policy Optimization（PPO）等算法，并引入迁移学习与循环神经网络（RNN）的改进方案。结果表明，尽管DQN可作为有效的策略学习基线，但集成ResNet与LSTM模型显著提升了智能体对复杂时空动态的建模能力。PPO在连续动作空间中表现出良好控制性能，但面临策略坍塌等挑战。研究比较了不同方法的性能，并提出未来方向：提升计算效率与模型稳定性。成果有助于推动自动驾驶及相关控制任务中的AI系统发展。
原文摘要 · Abstract (English)
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.
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