Modular Adaptive Policy Selection for Multi-Task Imitation Learning through Task Division
This addresses the challenge of requiring many expert demonstrations for deep imitation learning across multiple tasks, though it is incremental as it builds on existing multi-task learning approaches.
The paper tackles the problem of negative transfer in multi-task imitation learning by introducing a method that uses proto-policies as modules to divide tasks into shared and task-specific sub-behaviors, with experiments showing improved accuracy over single agents, task-conditioned agents, multi-headed agents, and state-of-the-art meta-learning agents.
Deep imitation learning requires many expert demonstrations, which can be hard to obtain, especially when many tasks are involved. However, different tasks often share similarities, so learning them jointly can greatly benefit them and alleviate the need for many demonstrations. But, joint multi-task learning often suffers from negative transfer, sharing information that should be task-specific. In this work, we introduce a method to perform multi-task imitation while allowing for task-specific features. This is done by using proto-policies as modules to divide the tasks into simple sub-behaviours that can be shared. The proto-policies operate in parallel and are adaptively chosen by a selector mechanism that is jointly trained with the modules. Experiments on different sets of tasks show that our method improves upon the accuracy of single agents, task-conditioned and multi-headed multi-task agents, as well as state-of-the-art meta learning agents. We also demonstrate its ability to autonomously divide the tasks into both shared and task-specific sub-behaviours.