Основные понятия
R2I integrates state space models to enhance long-term memory and credit assignment in model-based reinforcement learning, achieving superior performance across diverse tasks.
Аннотация
R2I introduces a new method, Recall to Imagine (R2I), integrating state space models (SSMs) into world models of model-based reinforcement learning agents. This integration aims to improve temporal coherence, long-term memory, and credit assignment. Through various tasks, R2I establishes a new state-of-the-art for challenging memory and credit assignment RL tasks. It showcases superhuman performance in the complex Memory Maze domain while maintaining comparable performance in classic RL tasks like Atari and DMC. R2I is faster than the state-of-the-art MBRL method, DreamerV3, resulting in faster convergence time.
Статистика
R2I showcases superhuman performance in the Memory Maze domain.
R2I is faster than DreamerV3, resulting in faster wall-time convergence.
Цитаты
R2I not only establishes a new state-of-the-art for challenging memory and credit assignment RL tasks but also showcases superhuman performance in the complex Memory Maze.
R2I is faster than the state-of-the-art MBRL method, DreamerV3, resulting in faster wall-time convergence.