Option-aware Temporally Abstracted Value for Offline Goal-Conditioned
  Reinforcement Learning
Authors: name: Hongjoon Ahn, name: Heewoong Choi, name: Jisu Han, name: Taesup Moon

Offline goal-conditioned reinforcement learning (GCRL) offers a practical
learning paradigm where goal-reaching policies are trained from abundant
unlabeled (reward-free) datasets without additional environment interaction.
However, offline GCRL still struggles with long-horizon tasks, even with recent
advances that employ hierarchical policy structures, such as HIQL. By
identifying the root cause of this challenge, we observe the following
insights: First, performance bottlenecks mainly stem from the high-level
policy's inability to generate appropriate subgoals. Second, when learning the
high-level policy in the long-horizon regime, the sign of the advantage signal
frequently becomes incorrect. Thus, we argue that improving the value function
to produce a clear advantage signal for learning the high-level policy is
essential. In this paper, we propose a simple yet effective solution:
Option-aware Temporally Abstracted value learning, dubbed OTA, which
incorporates temporal abstraction into the temporal-difference learning
process. By modifying the value update to be option-aware, the proposed
learning scheme contracts the effective horizon length, enabling better
advantage estimates even in long-horizon regimes. We experimentally show that
the high-level policy extracted using the OTA value function achieves strong
performance on complex tasks from OGBench, a recently proposed offline GCRL
benchmark, including maze navigation and visual robotic manipulation
environments.