OffRIPP：基于离线强化学习的启发式路径规划
OffRIPP: Offline RL-based Informative Path Planning
AI总结：
提出基于离线强化学习的IPP框架OffRIPP，利用批量约束RL缓解外推误差，在无需实时交互下优化信息增益，兼具安全性与高效性，实验证明其优于基线方法。
摘要：
信息路径规划（IPP）是机器人领域的一项关键任务，智能体必须设计路径以在遵守资源约束的同时收集关于目标环境的宝贵信息。强化学习（RL）已被证明对IPP有效，然而它需要环境交互，这在实际应用中既冒险又昂贵。为解决这一问题，我们提出了一种基于离线RL的IPP框架，该框架在训练期间无需实时交互即可优化信息增益，通过避免交互提供了安全性和成本效益，并在执行期间具备卓越的性能和快速的计算能力——这些都是RL的关键优势。我们的框架利用批量约束强化学习来缓解外推误差，使智能体能够从由任意算法生成的预收集数据集中学习。我们通过广泛的模拟和真实世界实验验证了该框架。数值结果表明，我们的框架优于基线方法，证明了所提方法的有效性。
英文摘要：
Informative path planning (IPP) is a crucial task in robotics, where agents must design paths to gather valuable information about a target environment while adhering to resource constraints. Reinforcement learning (RL) has been shown to be effective for IPP, however, it requires environment interactions, which are risky and expensive in practice. To address this problem, we propose an offline RL-based IPP framework that optimizes information gain without requiring real-time interaction during training, offering safety and cost-efficiency by avoiding interaction, as well as superior performance and fast computation during execution -- key advantages of RL. Our framework leverages batch-constrained reinforcement learning to mitigate extrapolation errors, enabling the agent to learn from pre-collected datasets generated by arbitrary algorithms. We validate the framework through extensive simulations and real-world experiments. The numerical results show that our framework outperforms the baselines, demonstrating the effectiveness of the proposed approach.
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。
