潜在エネルギー行動計画とワールドモデル
Latent Energy Action Planning with World Models
潜在ワールドモデルを用いたモデル予測制御において、行動系列全体を最適化変数として扱い、終端状態のエネルギー最小化と目標記述子の一致を組み合わせる新しい計画手法LEAPを提案。4つの制御ドメインで成功率を77.5%から94.8%に向上させた。
著者: Phu Pham, Aniket Bera
分類: cs.LG
原文アブストラクト
Latent world models support efficient model predictive control from high-dimensional observations, yet optimizing a single learned latent objective can favor action sequences whose decoder-predicted terminal descriptor does not match the goal descriptor. We introduce Latent Energy Action Planning (LEAP), which treats the complete action horizon as a differentiable variable and optimizes it through a frozen LeWorldModel (LeWM). LEAP couples terminal latent goal matching with a terminal-window state energy. Low energy requires the predicted terminal latent to agree with the goal latent and the decoder-predicted terminal descriptor to agree with the goal descriptor. A frozen goal-conditioned proposal initializes the search, a quasi-Newton solver refines actions through the autoregressive rollout, and post-optimization projection enforces the admissible action range. Across four control domains using the officially released LeWM checkpoints, the complete LEAP planning system raises mean success from 77.5% for LeWM planned with the cross-entropy method (LeWM+CEM) to 94.8% under a matched protocol, a 17.3-percentage-point improvement, while retaining the frozen LeWM representation and predictor.