超球面コントラスティブ表現による計画と探索の実現
Hyperspherical Contrastive Representations Unlock Planning and Exploration
コントラスティブ表現を超球面上で学習し、球面線形補間により中間ウェイポイントを閉形式で推論することで、報酬やデモなしに長期タスクを計画・探索する手法を提案。
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: Jonghae Park, Sungil Son, Jonghun Shin, Junseo Lee, H. Jin Kim, Benjamin Eysenbach
分類: cs.LG
原文アブストラクト
We develop a self-supervised reinforcement learning algorithm that learns to build towers of up to eight cubes from scratch, without prior data, rewards, demonstrations, or pretrained models. Our method, Contrastive Planning via Spherical Linear Interpolation (C-Slerp), uses contrastive representations as reusable building blocks and repeatedly composes them to infer a sequence of intermediate waypoints. Under our hyperspherical parameterization, the learned representations allow intermediate waypoints to be inferred in closed form through spherical linear interpolation, without training a separate high-level policy or performing graph search. Importantly, the representation geometry also tells us how to adjust the number of waypoints based on the difficulty of each goal-reaching problem. Experiments show that our method succeeds on long-horizon navigation and robotic manipulation tasks, including settings where strong baselines fail to achieve a single success. Ablations further show that using a fixed number of waypoints substantially degrades performance, highlighting the importance of adapting the plan to task difficulty.