Sharing Lifelong Reinforcement Learning Knowledge via Modulating Masks
Saptarshi Nath Christos Peridis Eseoghene Ben-Iwhiwhu Xinran Liu Shirin Dora Cong Liu Soheil Kolouri Andrea Soltoggio
Lifelong learning agents aim to learn multiple tasks sequentially over a
lifetime. This involves the ability to exploit previous knowledge when learning
new tasks and to avoid forgetting. Modulating masks, a specific type of
parameter isolation approach, have recently shown promise in both supervised
and reinforcement learning. While lifelong learning algorithms have been
investigated mainly within a single-agent approach, a question remains on how
multiple agents can share lifelong learning knowledge with each other. We show
that the parameter isolation mechanism used by modulating masks is particularly
suitable for exchanging knowledge among agents in a distributed and
decentralized system of lifelong learners. The key idea is that the isolation
of specific task knowledge to specific masks allows agents to transfer only
specific knowledge on-demand, resulting in robust and effective distributed
lifelong learning. We assume fully distributed and asynchronous scenarios with
dynamic agent numbers and connectivity. An on-demand communication protocol
ensures agents query their peers for specific masks to be transferred and
integrated into their policies when facing each task. Experiments indicate that
on-demand mask communication is an effective way to implement distributed
lifelong reinforcement learning and provides a lifelong learning benefit with
respect to distributed RL baselines such as DD-PPO, IMPALA, and PPO+EWC. The
system is particularly robust to connection drops and demonstrates rapid
learning due to knowledge exchange.