Dynamic Sparsity: Challenging Common Sparsity Assumptions for Learning World Models in Robotic Reinforcement Learning Benchmarks

- The research critically examines the assumptions of sparsity in dynamics models used in robotic reinforcement learning, particularly in the MuJoCo Playground benchmark. It finds that global sparsity is rare, suggesting that learning may not benefit from sparsity priors as previously thought. This development is significant as it challenges established beliefs in the field, potentially leading to new approaches in reinforcement learning that do not rely on sparsity assumptions.