Replay-enhanced Continual Reinforcement Learning
Tiantian Zhang Kevin Zehua Shen Zichuan Lin Bo Yuan Xueqian Wang Xiu Li Deheng Ye
Replaying past experiences has proven to be a highly effective approach for
averting catastrophic forgetting in supervised continual learning. However,
some crucial factors are still largely ignored, making it vulnerable to serious
failure, when used as a solution to forgetting in continual reinforcement
learning, even in the context of perfect memory where all data of previous
tasks are accessible in the current task. On the one hand, since most
reinforcement learning algorithms are not invariant to the reward scale, the
previously well-learned tasks (with high rewards) may appear to be more salient
to the current learning process than the current task (with small initial
rewards). This causes the agent to concentrate on those salient tasks at the
expense of generality on the current task. On the other hand, offline learning
on replayed tasks while learning a new task may induce a distributional shift
between the dataset and the learned policy on old tasks, resulting in
forgetting. In this paper, we introduce RECALL, a replay-enhanced method that
greatly improves the plasticity of existing replay-based methods on new tasks
while effectively avoiding the recurrence of catastrophic forgetting in
continual reinforcement learning. RECALL leverages adaptive normalization on
approximate targets and policy distillation on old tasks to enhance generality
and stability, respectively. Extensive experiments on the Continual World
benchmark show that RECALL performs significantly better than purely perfect
memory replay, and achieves comparable or better overall performance against
state-of-the-art continual learning methods.