Autonomous robot car with Deep Reinforcement Learning

Yi Feng

AutorIn

Yi Feng

Titel

Autonomous robot car with Deep Reinforcement Learning

Zitierfähige Url:

https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-949923

Erstveröffentlichung

2024

Datum der Einreichung

26.11.2024

Datum der Verteidigung

11.12.2024

Abstract (EN)

Autonomous driving (AD) aims to achieve fully autonomous operation in various complex traffic environments. Deep reinforcement learning (DRL) integrates the perception capabilities of deep learning and the decision-making capabilities of reinforcement learning (RL), providing efficient solutions for autonomous driving through non-end-to-end pretraining methods and end-to-end direct control methods. This study utilizes the Gym-Duckietown simulation platform and applies Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) algorithms to compare and evaluate the performance of non-end-to-end and end-to-end DRL models across tasks of varying complexity. The main research results highlight significant performance differences based on input dimensionality, RL algorithm selection, and reward structure. Notably, in non-end-to-end complex tasks, using directly loaded pretrained weights from simple models significantly enhances model generalization. In end-to-end complex task models, phased reinforcement learning strategies demonstrate advantages over standard training methods. The experimental results reveal the critical influence of reward design, task complexity, and RL algorithms on model performance, and demonstrate the potential of phased reinforcement learning in improving training efficiency and adaptability. These findings validate the applicability of DRL in AD and provide insights for optimizing training strategies, reward structures, and algorithm selection in future AD research.

Freie Schlagwörter (EN)

Deep Reinforcement Learning, Autonomous driving, End-to-end, Proximal Policy Optimization, Soft Actor-Critic, Lane keeping, Traffic light recognition.

Klassifikation (DDC)

380

Klassifikation (RVK)

ZO 4660

GutachterIn