  Continual reinforcement learning aims to sequentially learn a variety of
tasks, retaining the ability to perform previously encountered tasks while
simultaneously developing new policies for novel tasks. However, current
continual RL approaches overlook the fact that certain tasks are identical
under basic group operations like rotations or translations, especially with
visual inputs. They may unnecessarily learn and maintain a new policy for each
similar task, leading to poor sample efficiency and weak generalization
capability. To address this, we introduce a unique Continual Vision-based
Reinforcement Learning method that recognizes Group Symmetries, called COVERS,
cultivating a policy for each group of equivalent tasks rather than individual
tasks. COVERS employs a proximal policy optimization-based RL algorithm with an
equivariant feature extractor and a novel task grouping mechanism that relies
on the extracted invariant features. We evaluate COVERS on sequences of
table-top manipulation tasks that incorporate image observations and robot
proprioceptive information in both simulations and on real robot platforms. Our
results show that COVERS accurately assigns tasks to their respective groups
and significantly outperforms existing methods in terms of generalization
capability.
