Research
Berkeley AI Research's GRASP Planner Makes Long-Horizon World Model Planning More Robust
Berkeley AI Research researchers propose GRASP (Gradient RelAxed Stochastic Planner), a gradient-based planner for learned world models that targets long-horizon planning. GRASP lifts the dynamics constraint into virtual states for parallel, collocation-style optimization, injects Gaussian noise into state iterates for exploration, and avoids brittle state-input Jacobians by stopping gradients through the world model's state input while retaining action gradients. The paper reports improved succ
world modelsplanningmodel-based reinforcement learning