RAMP: Hybrid DRL for Online Learning of Numeric Action Models

Humans solve complex problems by constructing plans and mentally simulating their outcomes with an internal model of the world. Machine learning has made substantial progress in learning world models that predict the consequences of action sequences, yet the procedures used to plan with these models remain largely hand-designed. Most planners rely on fixed search or optimization rules; approaches that learn aspects of search typically imitate a predefined optimizer or use planning to inform an amortized policy, rather improving multi-step plans. We introduce Reinforced Planning, a method that learns the plan-update itself by reinforcing update rules that produce better plans, using gradients propagated through a differentiable world model. We instantiate Reinforced Planning in RP1, which learns a critic over imagined outcomes via temporal-difference learning and a neural plan-improvement operator trained via imagined rollouts with a pretrained world model. RP1 can be trained fully offline without environment interaction; environment episodes are used only for checkpoint selection. Across visual navigation, arm reaching, and robotic manipulation on two world-model backbones, RP1 matches or exceeds existing planners, achieving near-perfect success in several settings while using $1{,}000\times$ fewer world-model rollouts than the strongest alternative (CEM) and planning up to $67\times$ faster under concurrent planners inference.

Reinforcement Learning (RL) policies often degrade in unfamiliar environments because they lack explicit deliberation. We propose Plan, Align, Commit, Think (PACT), a hybrid architecture that combines a fast, reactive RL policy with a slow, deliberative Small Language Model (SLM) planner. PACT invokes the SLM asynchronously to generate and validate candidate action plans. Once a plan is verified through simulation as safe, feasible, and complete, it is executed directly, bypassing the RL policy without retraining or modifying it. Evaluated on three FrozenLake configurations of increasing difficulty, PACT outperforms all baselines while relying on a 2B-parameter SLM backbone, suggesting that deliberative planning and reactive execution are more powerful in concert than either is alone in these settings.

Accurate action models are critical for effective planning. Existing action-model learning methods largely assume simple action representations or become computationally intractable when learning conditional and quantified effects. We present Online Hypothesis-Driven Conditional Action Model Learning (OHCAM), an online approach for learning action models with conditional and quantified effects from limited interactions with the environment. OHCAM maintains a belief over hypothesized action models and actively selects informative actions to reduce uncertainty by maximizing disagreement among competing hypotheses, while being robust to noisy observations. To enable scalability, OHCAM begins with a small set of simple action model hypotheses and expands to more complex conditions only when the current hypotheses become inconsistent with the data. Experiments on six benchmark planning domains demonstrate that OHCAM is sample efficient in learning action models that solve substantially more tasks than baselines, even with observation noise. We validate OHCAM on two tasks using a Kinova Gen3 robot, demonstrating the real-world applicability of our approach.