Modular Adaptive Policy Selection for Multi-Task Imitation Learning through Task Division. (arXiv:2203.14855v2 [cs.LG] UPDATED)

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 …

arxiv imitation learning learning modular policy