We propose a method that enables large language models (LLMs) to control embodied agents through the generation of control policies that directly map continuous observation vectors to continuous action vectors. At the outset, the LLMs generate a control strategy based on a textual description of the agent, its environment, and the intended goal. This strategy is then iteratively refined through a learning process in which the LLMs are repeatedly prompted to improve the current strategy, using performance feedback and sensory-motor data collected during its evaluation. The method is validated on classic control tasks from the Gymnasium library and the inverted pendulum task from the MuJoCo library. The approach proves effective with relatively compact models such as GPT-oss:120b and Qwen2.5:72b. In most cases, it successfully identifies optimal or near-optimal solutions by integrating symbolic knowledge derived through reasoning with sub-symbolic sensory-motor data gathered as the agent interacts with its environment.

翻译：我们提出一种方法，使大语言模型（LLMs）能够通过生成直接将连续观测向量映射至连续动作向量的控制策略来控制具身智能体。该方法首先基于智能体、环境及预期目标的文本描述生成控制策略，随后通过迭代学习过程进行优化：在此过程中，系统持续利用性能反馈及策略评估期间收集的感觉运动数据，提示大语言模型改进当前策略。该方法在Gymnasium库的经典控制任务和MuJoCo库的倒立摆任务中得到验证。实验表明，该方法在GPT-oss:120b和Qwen2.5:72b等相对紧凑的模型上表现有效。在多数情况下，该方法通过整合推理产生的符号知识与智能体与环境交互时收集的亚符号感觉运动数据，成功寻得最优或接近最优的解决方案。