无需人工设计奖励信号，直接通过状态预测实现车辆控制优化。
Towards a Reward-Free Reinforcement Learning Framework for Vehicle Control
- 用目标状态预测网络和无奖励策略网络替代人工奖励设计
- 通过最小化预测状态与专家状态差异来训练策略，提升学习效率
- 适合缺乏明确奖励信号的车辆控制场景，降低人为偏差
强化学习在车辆控制中通过设计或学习合适的奖励信号指导智能体学习最优控制策略。然而，在车辆控制应用中，奖励通常需手动设计并考虑多种隐含因素，易引入人为偏见。尽管模仿学习不依赖显式奖励信号，但需要高质量专家动作，而这类数据往往难以获取。为此，我们提出一种无奖励强化学习框架（RFRLF）。该框架通过目标状态预测网络（TSPN）和无奖励状态引导策略网络（RFSGPN），直接学习目标状态以优化智能体行为，避免对人工设计奖励信号的依赖。具体而言，策略网络通过最小化预测状态与专家状态之间的差异进行训练。实验结果表明，所提方法在车辆驾驶控制中有效，展现出提升学习效率和适应无奖励环境的优势。
原文摘要 · Abstract (English)
Reinforcement learning plays a crucial role in vehicle control by guiding agents to learn optimal control strategies through designing or learning appropriate reward signals. However, in vehicle control applications, rewards typically need to be manually designed while considering multiple implicit factors, which easily introduces human biases. Although imitation learning methods does not rely on explicit reward signals, they necessitate high-quality expert actions, which are often challenging to acquire. To address these issues, we propose a reward-free reinforcement learning framework (RFRLF). This framework directly learns the target states to optimize agent behavior through a target state prediction network (TSPN) and a reward-free state-guided policy network (RFSGPN), avoiding the dependence on manually designed reward signals. Specifically, the policy network is learned via minimizing the differences between the predicted state and the expert state. Experimental results demonstrate the effectiveness of the proposed RFRLF in controlling vehicle driving, showing its advantages in improving learning efficiency and adapting to reward-free environments.
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