Goal Recognition using Actor-Critic Optimization

TL;DR

DRACO is a novel deep reinforcement learning approach for goal recognition that learns policy networks from unstructured data, outperforming existing methods in both discrete and continuous environments with reduced computational costs.

Contribution

It introduces the first goal recognition algorithm that learns policy networks from unstructured data and uses continuous policy representations for inference.

Findings

Achieves state-of-the-art performance in discrete settings.

Outperforms existing methods in continuous environments.

Reduces computational and memory costs significantly.

Abstract

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.