LearningFlow: 大規模言語モデルによる都市運転のための自動方策学習ワークフロー
LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models
複数のLLMエージェントが協調してカリキュラムと報酬関数を自動生成し、都市運転の強化学習方策を効率的に学習させるフレームワークを提案した。
著者: Zengqi Peng, Yubin Wang, Xu Han, Lei Zheng, Jun Ma
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Recent advancements in reinforcement learning (RL) demonstrate the significant potential in autonomous driving. Despite this promise, challenges such as the manual design of reward functions and low sample efficiency in complex environments continue to impede the development of safe and effective driving policies. To tackle these issues, we introduce LearningFlow, an innovative automated policy learning workflow tailored to urban driving. This framework leverages the collaboration of multiple large language model (LLM) agents throughout the RL training process. LearningFlow includes a curriculum sequence generation process and a reward generation process, which work in tandem to guide the RL policy by generating tailored training curricula and reward functions. Particularly, each process is supported by an analysis agent that evaluates training progress and provides critical insights to the generation agent. Through the collaborative efforts of these LLM agents, LearningFlow automates policy learning across a series of complex driving tasks, and it significantly reduces the reliance on manual reward function design while enhancing sample efficiency. Comprehensive experiments are conducted in the high-fidelity CARLA simulator, along with comparisons with other existing methods, to demonstrate the efficacy of our proposed approach. The results demonstrate that LearningFlow excels in generating rewards and curricula. It also achieves superior performance and robust generalization across various driving tasks, as well as commendable adaptation to different RL algorithms.
