Differentiable Learning of Lifted Action Schemas for Classical Planning

1. 1Apply differentiable learning to lifted action schemas using neural networks to learn compact representations
2. 2Use STRIPS or PDDL to represent large deterministic MDPs as sets of atoms over objects and relations
3. 3Implement lifted action schemas to add or delete atoms in the planning domain
4. 4Evaluate the performance of the learned planning model using search heuristics and structural generalization metrics
5. 5Integrate the differentiable learning approach with existing classical planning algorithms to improve their efficiency and scalability

Researchers and engineers working on classical planning and reinforcement learning can benefit from this approach to improve the efficiency and scalability of their planning models

💡 Differentiable learning of lifted action schemas enables more efficient and generalizable planning models by leveraging the compact representation of classical planning problems

🤖 Learn to apply differentiable learning to lifted action schemas for classical planning! 🚀 Improve efficiency and scalability of planning models with neural networks 📈