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的基础。在强化学习中，目标条件强化学习有望为利用大量无标签（无奖励）数据提供类似的自我监督方法。然而，构建能够直接从多样化离线数据中学习的高效目标条件强化学习算法颇具挑战，因为难以精确估计遥远目标的真实价值函数。但目标达成问题具有内在结构——达到遥远目标需先经过更近的子目标。这种结构极具价值，因为评估近处目标的动作质量通常比远处目标更容易。基于这一思路，我们提出一种从离线数据中进行目标条件强化学习的层次化算法。通过使用一个无需动作的价值函数，我们学习两个能够利用该结构的策略：高层策略将状态视为动作并预测子目标（隐表示），低层策略则预测达成该子目标所需的动作。通过理论分析与教学案例，我们展示了这种层次化分解如何使方法对价值函数估计噪声具有鲁棒性。随后将方法应用于离线目标达成基准测试，结果表明：我们的方法可解决此前方法难以应对的长周期任务、可扩展至高维图像观测，并能灵活利用无动作数据。代码已开源至 https://seohong.me/projects/hiql/