Unsupervised pre-training has recently become the bedrock for computer vision and natural language processing. In reinforcement learning (RL), goal-conditioned RL can potentially provide an analogous self-supervised approach for making use of large quantities of unlabeled (reward-free) data. However, building effective algorithms for goal-conditioned RL that can learn directly from diverse offline data is challenging, because it is hard to accurately estimate the exact value function for faraway goals. Nonetheless, goal-reaching problems exhibit structure, such that reaching distant goals entails first passing through closer subgoals. This structure can be very useful, as assessing the quality of actions for nearby goals is typically easier than for more distant goals. Based on this idea, we propose a hierarchical algorithm for goal-conditioned RL from offline data. Using one action-free value function, we learn two policies that allow us to exploit this structure: a high-level policy that treats states as actions and predicts (a latent representation of) a subgoal and a low-level policy that predicts the action for reaching this subgoal. Through analysis and didactic examples, we show how this hierarchical decomposition makes our method robust to noise in the estimated value function. We then apply our method to offline goal-reaching benchmarks, showing that our method can solve long-horizon tasks that stymie prior methods, can scale to high-dimensional image observations, and can readily make use of action-free data. Our code is available at https://seohong.me/projects/hiql/

翻译：无监督预训练近期已成为计算机视觉和自然语言处理领域的基础。在强化学习中，目标条件强化学习可提供一种类似的自我监督方法，以利用大量无标签（无奖励）数据。然而，构建能够直接从多样化离线数据中学习的有效目标条件强化学习算法颇具挑战性，原因在于准确估计远处目标的价值函数十分困难。尽管如此，目标达成问题具有结构性特征：到达遥远目标需要先经过较近的子目标。这一结构非常有用，因为评估近处目标的动作质量通常比评估远处目标更容易。基于此思想，我们提出了一种利用离线数据进行目标条件强化学习的层次化算法。通过使用一个无动作价值函数，我们学习两个策略以利用该结构：一个将状态视为动作并预测子目标（的隐表示）的高层策略，以及一个预测达成该子目标动作的低层策略。通过理论分析和示例说明，我们展示了这种层次分解如何使我们的方法对价值函数估计噪声具有鲁棒性。随后我们将该方法应用于离线目标达成基准测试，结果表明我们的方法能解决前任方法难以解决的长期任务，可扩展至高维图像观测，并能有效利用无动作数据。我们的代码发布于https://seohong.me/projects/hiql/。