Title: Improving Learning from Demonstrations by Learning from Experience. Abstract:     How to make imitation learning more general when demonstrations are relative limited has been a persistent problem in reinforcement learning (RL). Poor demonstrations leads to narrow and biased date distribution, non-Markovian human expert demonstration makes it difficult for the agent to learn, and over-reliance on sub-optimal trajectories can make it hard for the agent to improve its performance. To solve these problems we propose a new algorithm named TD3fG that can smoothly transition from learning from experts to learning from experience. Our algorithm achieve good performance in mujoco environment with limited and sub-optimal demonstrations.
    We use behavior cloning to train network as a reference action generator and utilize it in terms of both loss function and exploration noise. This innovation can help agents extract a priori knowledge from demonstrations while reducing the detrimental effects of the poor Markovian properties of the demonstrations. It has better performance compared to the BC+ fine-tuning and DDPGfD approach, especially when the demonstrations are relatively limited. We call our method TD3fG meaning TD3 from a generator.