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VaDar: Implicit Goal Conditioning via Value Disaggregation

Shashwat Saxena, Mehul Goel, Sreyas Venkataraman, Sarvesh Patil, Max Simchowitz · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26629 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

長期的な課題は現代のAIとロボット技術にとって重要ですが、強化学習の課題となっています。歴史的に、一般的なアプローチは、課題をサブゴールに分解し、それぞれのサブタスクを連続的に実行するためのサブゴール条件のポリシーを学ぶことです。我々は、Value Disaggregation(VaDar)と呼ばれる選択肢を導入しますが、単一のサブゴール独立のポリシーを訓練して、サブゴール毎の批判的価値の線形的な組み合わせを最大限にします。サブゴール情報は訓練時間にのみ必要であるため、VaDarは、可能なかつ困難なサブゴール選択の必要性を排除します。さらに、慎重な実験を通じて、VaDarは

Machine translation (Meta M2M100); the English original follows.

Abstract

Long-horizon tasks are important for modern AI and robotics, yet pose a challenge for reinforcement learning. Historically, a popular approach has been to decompose a task into subgoals, and learn a subgoal-conditioned policy to execute each subtask in sequence. We introduce an alternative called Value Disaggregation (VaDar), which trains a single subgoal-independent policy to maximize a linear combination of per-subgoal critic values. Because subgoal information is needed only at training-time, VaDar removes the need for possibly costly or cumbersome subgoal-selection during policy deployment. Moreover, through careful experiments, we show that VaDar (1) is always competitive with, and frequently outperforms, natural baselines like goal-conditioning and (2) succeeds on tasks with non-sequential goal structure and multiple optimization objectives. In particular, VaDar is the first algorithm to solve challenging, long-horizon tasks in the RoboCasa suite, even when starting from a policy pretrained from behavior cloning with near-zero initial task success. Taken together, VaDar's successes suggest that subgoal information benefits actor-critic reinforcement learning by enhancing the efficacy of critic learning, whereas policy goal-conditioning is often unnecessary.

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Topics: Reinforcement learning, Task (project management), Computer science, Artificial intelligence, Value (mathematics), Machine learning

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