Safe CoR：一种利用约束奖励整合模仿学习与安全强化学习的双专家方法
Safe CoR: A Dual-Expert Approach to Integrating Imitation Learning and Safe Reinforcement Learning Using Constraint Rewards
AI总结：
本文提出Safe CoR双专家框架，利用奖励与安全专家演示及约束奖励平衡性能和安全，在仿真与真实Jackal平台上验证可显著提升性能并减少约束违反。
摘要：
在自主智能体领域，确保复杂动态环境中的安全性与可靠性仍是一项首要挑战。安全强化学习通过引入安全约束来应对这些问题，但在复杂驾驶场景等错综复杂的环境中进行导航时仍面临挑战。为克服这些挑战，我们提出了安全约束奖励（Safe CoR）框架，这是一种利用两类专家演示的新方法：聚焦性能优化的奖励专家演示，以及优先考虑安全的安全专家演示。通过利用约束奖励（CoR），该框架引导智能体在奖励总和的性能目标与安全约束之间取得平衡。我们在多种环境中测试了所提出的框架，包括 safety gym、metadrive 以及真实世界的 Jackal 平台。所提框架在真实世界 Jackal 平台上将算法性能提升了39%，并将约束违反减少了88%，证明了该框架的有效性。通过这一创新方法，我们期望真实世界性能取得显著进展，从而在安全可靠的自主智能体领域产生变革性影响。
英文摘要：
In the realm of autonomous agents, ensuring safety and reliability in complex and dynamic environments remains a paramount challenge. Safe reinforcement learning addresses these concerns by introducing safety constraints, but still faces challenges in navigating intricate environments such as complex driving situations. To overcome these challenges, we present the safe constraint reward (Safe CoR) framework, a novel method that utilizes two types of expert demonstrations$\unicode{x2013}$reward expert demonstrations focusing on performance optimization and safe expert demonstrations prioritizing safety. By exploiting a constraint reward (CoR), our framework guides the agent to balance performance goals of reward sum with safety constraints. We test the proposed framework in diverse environments, including the safety gym, metadrive, and the real$\unicode{x2013}$world Jackal platform. Our proposed framework enhances the performance of algorithms by $39\%$ and reduces constraint violations by $88\%$ on the real-world Jackal platform, demonstrating the framework's efficacy. Through this innovative approach, we expect significant advancements in real-world performance, leading to transformative effects in the realm of safe and reliable autonomous agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
