Self-Driving Car Racing: Application of Deep Reinforcement Learning

TL;DR

This paper investigates deep reinforcement learning algorithms like DQN and PPO for autonomous car racing in simulation, highlighting how advanced models improve control and spatial understanding, with insights into their performance and stability challenges.

Contribution

The paper compares multiple RL algorithms and introduces adaptations like transfer learning and RNNs to enhance autonomous racing performance.

Findings

DQN provides a strong baseline for policy learning.

Integrating ResNet and LSTM improves spatial and temporal understanding.

PPO shows promise in continuous control but faces stability issues.

Abstract

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.