目標条件付き方策学習におけるホライズンの情報的呪い
An Informational Curse of Horizon in Goal-Conditioned Policy Learning
目標再ラベリングのホライズンが長くなると方策の汎化性能が低下する現象を「情報的呪い」として特定し、入力ヤコビアンの蒸留で改善できることを示した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: John L. Zhou, Yuxuan Dong, Jonathan C. Kao
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
The difficulty of learning goal-reaching policies is often attributed to a "curse of horizon" that manifests as bias accumulation in temporal-difference backups and noisy advantage estimates. In this work, we identify an additional informational curse of horizon in goal-conditioned policy learning, where increasing the goal relabeling horizon can significantly reduce policy generalization and performance. Through a series of controlled experiments with oracle planners, we decouple the goal horizons sampled during training from those that the policy is asked to reach at test time. Even when evaluated only on a sequence of nearby subgoals, goal-conditioned behavioral cloning (BC) policies suffer from severe, training horizon-dependent performance degradation that is mitigated by reinforcement learning (RL) objectives. We explain this phenomenon as a horizon-dependent decrease in the conditional mutual information between actions and hindsight-relabeled goals, and find empirically that both BC and RL policies trained on longer-horizon goals exhibit a shift in sensitivity from goal to state information, as measured by the policy's input Jacobians. Motivated by this observation, we find that distilling the input Jacobians of short-horizon policies into long-horizon policies yields significant performance gains, especially in combinatorial manipulation tasks. Taken together, our results highlight goal relabeling horizon as an important consideration when learning generalist policies from offline data.