Operating in the real-world often requires agents to learn about a complex
environment and apply this understanding to achieve a breadth of goals. This
problem, known as goal-conditioned reinforcement learning (GCRL), becomes
especially challenging for long-horizon goals. Current methods have tackled
this problem by augmenting goal-conditioned policies with graph-based planning
algorithms. However, they struggle to scale to large, high-dimensional state
spaces and assume access to exploration mechanisms for efficiently collecting
training data. In this work, we introduce Successor Feature Landmarks (SFL), a
framework for exploring large, high-dimensional environments so as to obtain a
policy that is proficient for any goal. SFL leverages the ability of successor
features (SF) to capture transition dynamics, using it to drive exploration by
estimating state-novelty and to enable high-level planning by abstracting the
state-space as a non-parametric landmark-based graph. We further exploit SF to
directly compute a goal-conditioned policy for inter-landmark traversal, which
we use to execute plans to "frontier" landmarks at the edge of the explored
state space. We show in our experiments on MiniGrid and ViZDoom that SFL
enables efficient exploration of large, high-dimensional state spaces and
outperforms state-of-the-art baselines on long-horizon GCRL tasks.

本文介绍 Successor Feature Landmarks（SFL），它是用于大型、高维空间的探索的一个框架，该框架利用继承特征（SF）的能力来驱动探索，估计状态新颖性，并通过将状态空间抽象为基于非参数地标的图表，启用高级别规划，实现了 GCRL 任务上的超越表现。