Parallel Stochastic Gradientbased Planning For World Models
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“We provide theoretical justification and experiments on video-based world models, where our resulting planner outperforms existing planning algorithms like the cross-entropy method (CEM) and vanilla gradient-based optimization (GD) on long-horizon experiments, both in success rate and time to convergence.”
GRASP: A Parallel Stochastic Gradient Planner for World Models ↗