The RAMP strategy integrates deep reinforcement learning with automated planning to learn numeric action models through direct environment interaction. It replaces the need for expert traces by training a policy and a model simultaneously. This feedback loop allows agents to plan future actions using refined models, reducing the manual effort required for domain specification.