  In this paper, we propose a multi-timescale replay (MTR) buffer for improving
continual learning in RL agents faced with environments that are changing
continuously over time at timescales that are unknown to the agent. The basic
MTR buffer comprises a cascade of sub-buffers that accumulate experiences at
different timescales, enabling the agent to improve the trade-off between
adaptation to new data and retention of old knowledge. We also combine the MTR
framework with invariant risk minimization, with the idea of encouraging the
agent to learn a policy that is robust across the various environments it
encounters over time. The MTR methods are evaluated in three different
continual learning settings on two continuous control tasks and, in many cases,
show improvement over the baselines.
