TASK GROUPING FOR REINFORCEMENT LEARNING WITH MULTIPLE TASKS

원문 (중국어)

To generate reinforcement learning (RL) policies for the multiple tasks performable by a system, a computing device is configured to train an RL model for all tasks of a system to produce a general RL model. For each task, the computing device updates the parameters of the general RL model based on the task to produce a task-specific RL model. Based on comparisons of the general RL model to the task-specific RL models, the computing device determines inter-task similarity scores that represent the impact of a task on other tasks, the impact of other tasks on a task, or both. The computing device then groups the tasks of the system together based on the inter-task similarity scores and generates a task-grouped RL policy for each group of tasks.