Abstract: Disclosed in the present disclosure are a transfer reinforcement learning method and apparatus, multi-task reinforcement learning method and apparatus, relating to the field of intelligent control technology. The transfer reinforcement learning method includes determining operational instructions for instructing an agent to perform a first task; determining an inclusion relation between multiple second tasks and the first tasks based on the operational instructions; determining a shared parameter set corresponding to the multiple second tasks based on the inclusion relation between the multiple second tasks and the first task, wherein the shared parameter set includes a plurality of parameters shared by the multiple second tasks; and performing transfer reinforcement learning based on the shared parameter set and the first task to obtain model parameters of a target policy model corresponding to the first task.

Abstract: A controller is provided for manipulating objects by a robot arm having a gripper. The controller includes a large language model (LLM) planner configured to acquire the states and the task description and generate an action sequence command that operates the robot arm with the gripper based on the task description, the current observations, historical information including historical actions and historical observations from previous steps.

Abstract: A controller is provided for manipulating objects by a robot arm having a gripper. The controller includes a large language model (LLM) planner configured to acquire the states and the task description and generate an action sequence command that operates the robot arm with the gripper based on the task description, the current observations, historical information including historical actions and historical observations from previous steps.

Abstract: Disclosed in the present disclosure are a transfer reinforcement learning method and apparatus, multi-task reinforcement learning method and apparatus, relating to the field of intelligent control technology. The transfer reinforcement learning method includes determining operational instructions for instructing an agent to perform a first task; determining an inclusion relation between multiple second tasks and the first tasks based on the operational instructions; determining a shared parameter set corresponding to the multiple second tasks based on the inclusion relation between the multiple second tasks and the first task, wherein the shared parameter set includes a plurality of parameters shared by the multiple second tasks; and performing transfer reinforcement learning based on the shared parameter set and the first task to obtain model parameters of a target policy model corresponding to the first task.

Abstract: Disclosed in the present disclosure are a method for training a multi-task model through multi-task reinforcement learning, an apparatus, an electronic device and a non-transitory computer readable storage medium. A method for training a multi-task model through multi-task reinforcement learning, including: acquiring observation signals observed for an environment by an agent; receiving T instructions each for instructing the agent to perform one of T tasks, T being a preset positive integer greater than 1; and generating K base policy models by performing training through multi-task reinforcement learning over a neural network based on the observation signals and the T instructions, wherein the K base policy models are combinable for generating respective task policy models for the T tasks to obtain the multi-task model for achieving the T tasks.