RAMP: Hybrid DRL for Online Learning of Numeric Action Models

RAMP proposes a novel strategy for learning numeric planning action models online through environmental interactions, integrating Deep Reinforcement Learning (DRL), action model learning, and planning. This creates a positive feedback loop where the RL policy gathers data to refine the action model, while the planner generates plans to continue training the RL policy.