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This preprint proposes using LLMs as offline designers that build RL pipelines (MDP formulation and simulator code) before deployment, rather than as runtime controllers. Its main claim is that executable code can still produce unlearnable policies when state, action, reward, and termination definitions are misaligned, and that staged, dependency-aware generation helps. The evidence is limited to experiments in Isaac Sim plus some secondary demos, so readers should check how well the gains transfer beyond that benchmark suite.
LLMs as MDP Designers: A Dependency-Aware Agentic Framework for Automated Robot Policy Generation
arXiv:2611.04974v1 Announce Type: cross Abstract: We investigate the automated generation of executable robot reinforcement learning (RL) policies from natural-language task descriptions. Rather than using large language models (LLMs) as execution-time decision makers, we use them as designers that construct RL pipelines before deployment, including Markov decision process (MDP) formulation and simulator implementation. We identify a phase-wise structure in this problem: MDP formulation is dependency-structured and sequential, whereas simulator implementation becomes modular once the formulation is fixed. Based on this structure, we propose ARPG, a dependency-aware agentic framework that performs sequential MDP modeling, localized verification and correction, and simulator-structured implementation while externalizing intermediate artifacts. ARPG targets a key failure mode of automated policy generation: executable code may still yield non-learnable policies when state, action, reward, and termination artifacts are not semantically aligned. Experiments in Isaac Sim show that ARPG improves modeling correctness, environment executability, and policy-generation success compared with monolithic LLM baselines and off-the-shelf agentic systems. Additional demonstrations on custom robotics, standard RL, and engineering-domain MDPs further suggest that the same modeling pipeline can be reused beyond the main benchmark suite.
Abstract:We investigate the automated generation of executable robot reinforcement learning (RL) policies from natural-language task descriptions. Rather than using large language models (LLMs) as execution-time decision makers, we use them as designers that construct RL pipelines before deployment, including Markov decision process (MDP) formulation and simulator implementation. We identify a phase-wise structure in this problem: MDP formulation is dependency-structured and sequential, whereas simulator implementation becomes modular once the formulation is fixed. Based on this structure, we propose ARPG, a dependency-aware agentic framework that performs sequential MDP modeling, localized verification and correction, and simulator-structured implementation while externalizing intermediate artifacts. ARPG targets a key failure mode of automated policy generation: executable code may still yield non-learnable policies when state, action, reward, and termination artifacts are not semantically aligned. Experiments in Isaac Sim show that ARPG improves modeling correctness, environment executability, and policy-generation success compared with monolithic LLM baselines and off-the-shelf agentic systems. Additional demonstrations on custom robotics, standard RL, and engineering-domain MDPs further suggest that the same modeling pipeline can be reused beyond the main benchmark suite.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2611.04974 [cs.RO]
(or arXiv:2611.04974v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2611.04974 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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