時間的表現による探索：外部報酬なしでの複雑な探索行動の学習
Temporal Representations for Exploration: Learning Complex Exploratory Behavior without Extrinsic Rewards
時間的コントラスト表現を用いて、未来の予測が難しい状態を優先的に探索する手法を提案し、移動・操作・身体化AIタスクで複雑な探索行動を学習できることを示した。
著者: Faisal Mohamed, Catherine Ji, Benjamin Eysenbach, Glen Berseth
分類: cs.LG, cs.AI
原文アブストラクト
Effective exploration in reinforcement learning requires not only tracking where an agent has been, but also understanding how the agent perceives and represents the world. To learn powerful representations, an agent should actively explore states that contribute to its knowledge of the environment. Temporal representations can capture the information necessary to solve a wide range of potential tasks while avoiding the computational cost associated with full state reconstruction. In this paper, we propose an exploration method that leverages temporal contrastive representations to guide exploration, prioritizing states with unpredictable future outcomes. We demonstrate that such representations can enable the learning of complex exploratory x in locomotion, manipulation, and embodied-AI tasks, revealing capabilities and behaviors that traditionally require extrinsic rewards. Unlike approaches that rely on explicit distance learning or episodic memory mechanisms (e.g., quasimetric-based methods), our method builds directly on temporal similarities, yielding a simpler yet effective strategy for exploration.