2026-10-12T00:08:04Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33868624 2026-09-16T23:19:24Z category_28840 category_28864 item_type_3 month_year_09_2026

Data for the paper titled "A Schema Bounded Language Model for Refining Robot Policies Without Destabilizing Local Learning" Chongwen Dong (24999742) Intelligent robotics Artificial intelligence not elsewhere classified decentralized multi robot systems Cross-LLM communication Large language models (LLMs), reinforcement learning inspired <p dir="ltr">Coordinating heterogeneous robots with large language models (LLMs) requires policy reasoning across task episodes and responsive motion control within each episode. These processes operate at different timescales, yet a practical architecture must connect shared experience to local adaptation without transferring action selection to a central model. This work examines the decentralized reuse of policy experience among robots guided by heterogeneous language models and develops a hierarchical framework for repeated policy refinement while retaining robot-local learning and action selection. Each robot owns a schema-bounded LLM policy agent, an Upper Confidence Bound (UCB) bandit, and a policy-conditioned Double Deep Q-Network (Double DQN) controller. At round boundaries, LLM agents exchange policy experience through a shared board, and UCB-guided refinement uses this context to update each robot’s local policy within a constrained schema. Within each round, Double DQN combines local state with the current language-derived policy to select actions through online correction. In simulation, the complete configuration achieved a 100\% goal-reaching rate across all 90 records, with the lowest median completion time of 42 ticks and a 90th-percentile completion time of 73.2 ticks. Its median completion time was 25.0–39.1\% lower than those of the comparison configurations. These results suggest that schema-bounded experience sharing supports repeated policy refinement while preserving robot-local learning and decentralized action selection.</p> 2026-09-16T23:19:24Z Dataset Dataset 10.6084/m9.figshare.33868624.v1 https://figshare.com/articles/dataset/Data_for_the_paper_titled_A_Schema_Bounded_Language_Model_for_Refining_Robot_Policies_Without_Destabilizing_Local_Learning_/33868624 CC BY 4.0