  Experience reuse is key to sample-efficient reinforcement learning. One of
the critical issues is how the experience is represented and stored.
Previously, the experience can be stored in the forms of features, individual
models, and the average model, each lying at a different granularity. However,
new tasks may require experience across multiple granularities. In this paper,
we propose the policy residual representation (PRR) network, which can extract
and store multiple levels of experience. PRR network is trained on a set of
tasks with a multi-level architecture, where a module in each level corresponds
to a subset of the tasks. Therefore, the PRR network represents the experience
in a spectrum-like way. When training on a new task, PRR can provide different
levels of experience for accelerating the learning. We experiment with the PRR
network on a set of grid world navigation tasks, locomotion tasks, and fighting
tasks in a video game. The results show that the PRR network leads to better
reuse of experience and thus outperforms some state-of-the-art approaches.
