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AI Research
BAIR Blog·Apr 20·5 min read
Gradient-based Planning for World Models at Longer Horizons
TL;DR
Planning over long time horizons in learned world models (neural networks trained to predict future states) is computationally expensive because optimization must simulate many sequential steps. GRASP solves this by lifting trajectories into virtual state spaces where optimization can run in parallel across time steps.
✦ Why It Matters
Engineers can now use gradient-based planning for long-horizon control tasks without prohibitive computational delays.
Key Takeaways
- Utilize GRASP for efficient long-horizon planning in AI applications.
- Incorporate stochasticity to enhance exploration during optimization.
- Optimize actions and states simultaneously for better performance.
- Address adversarial robustness issues in deep learning models.
How It Works
GRASP employs a collocation-based planning approach, treating dynamics constraints as soft penalties. It optimizes both actions and states, allowing for parallel computation and reducing the impact of adversarial robustness issues.
By injecting Gaussian noise into state updates, GRASP facilitates exploration while maintaining a focus on action gradients, leading to more reliable planning outcomes.