标题： 基于演员-评论家优化的目标识别 显示英文标题

标题： Goal Recognition using Actor-Critic Optimization

摘要： 目标识别旨在从观察序列中推断智能体的目标。现有的方法通常依赖于手动设计的领域和离散表示。使用深度强化学习的深度识别（DRACO）是一种新方法，它通过提供两个关键贡献克服了这些限制。首先，它是第一个从非结构化数据中学习一组策略网络并用于推理的目标识别算法。其次，DRACO通过连续策略表示引入了新的评估目标假设的度量。DRACO在离散设置中实现了目标识别的最先进性能，同时不使用现有方法所使用的结构化输入。此外，它在更具挑战性的连续设置中以显著降低的计算和内存成本超越了这些方法。这些结果共同展示了新算法的鲁棒性，弥合了传统目标识别和深度强化学习之间的差距。 显示英文摘要

摘要： Goal Recognition aims to infer an agent's goal from a sequence of observations. Existing approaches often rely on manually engineered domains and discrete representations. Deep Recognition using Actor-Critic Optimization (DRACO) is a novel approach based on deep reinforcement learning that overcomes these limitations by providing two key contributions. First, it is the first goal recognition algorithm that learns a set of policy networks from unstructured data and uses them for inference. Second, DRACO introduces new metrics for assessing goal hypotheses through continuous policy representations. DRACO achieves state-of-the-art performance for goal recognition in discrete settings while not using the structured inputs used by existing approaches. Moreover, it outperforms these approaches in more challenging, continuous settings at substantially reduced costs in both computing and memory. Together, these results showcase the robustness of the new algorithm, bridging traditional goal recognition and deep reinforcement learning.