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Combined with demonstrations, deep reinforcement learning can efficientlydevelop policies for manipulators. However, it takes time to collect sufficienthigh-quality demonstrations in practice. And human demonstrations may beunsuitable for robots. The non-Markovian process and over-reliance ondemonstrations are further challenges. For example, we found that RL agents aresensitive to demonstration quality in manipulation tasks and struggle to adaptto demonstrations directly from humans. Thus it is challenging to leveragelow-quality and insufficient demonstrations to assist reinforcement learning intraining better policies, and sometimes, limited demonstrations even lead toworse performance.  We propose a new algorithm named TD3fG (TD3 learning from a generator) tosolve these problems. It forms a smooth transition from learning from expertsto learning from experience. This innovation can help agents extract priorknowledge while reducing the detrimental effects of the demonstrations. Ouralgorithm performs well in Adroit manipulator and MuJoCo tasks with limiteddemonstrations.

Learning Complicated Manipulation Skills via Deterministic Policy with Limited Demonstrations