Forgetting and Imbalance in Robot Lifelong Learning with Off-policy Data. (arXiv:2204.05893v2 [cs.RO] UPDATED)

Robots will experience non-stationary environment dynamics throughout their
lifetime: the robot dynamics can change due to wear and tear, or its
surroundings may change over time. Eventually, the robots should perform well
in all of the environment variations it has encountered. At the same time, it
should still be able to learn fast in a new environment. We identify two
challenges in Reinforcement Learning (RL) under such a lifelong learning
setting with off-policy data: first, existing off-policy algorithms struggle
with …

arxiv data learning policy robot