  Multi-task learning is a very challenging problem in reinforcement learning.
While training multiple tasks jointly allow the policies to share parameters
across different tasks, the optimization problem becomes non-trivial: It
remains unclear what parameters in the network should be reused across tasks,
and how the gradients from different tasks may interfere with each other. Thus,
instead of naively sharing parameters across tasks, we introduce an explicit
modularization technique on policy representation to alleviate this
optimization issue. Given a base policy network, we design a routing network
which estimates different routing strategies to reconfigure the base network
for each task. Instead of directly selecting routes for each task, our
task-specific policy uses a method called soft modularization to softly combine
all the possible routes, which makes it suitable for sequential tasks. We
experiment with various robotics manipulation tasks in simulation and show our
method improves both sample efficiency and performance over strong baselines by
a large margin.
