  The purpose of multi-task reinforcement learning (MTRL) is to train a single
policy that can be applied to a set of different tasks. Sharing parameters
allows us to take advantage of the similarities among tasks. However, the gaps
between contents and difficulties of different tasks bring us challenges on
both which tasks should share the parameters and what parameters should be
shared, as well as the optimization challenges due to parameter sharing. In
this work, we introduce a parameter-compositional approach (PaCo) as an attempt
to address these challenges. In this framework, a policy subspace represented
by a set of parameters is learned. Policies for all the single tasks lie in
this subspace and can be composed by interpolating with the learned set. It
allows not only flexible parameter sharing but also a natural way to improve
training. We demonstrate the state-of-the-art performance on Meta-World
benchmarks, verifying the effectiveness of the proposed approach.
