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How to make imitation learning more general when demonstrations arerelatively limited has been a persistent problem in reinforcement learning(RL). Poor demonstrations lead to narrow and biased date distribution,non-Markovian human expert demonstration makes it difficult for the agent tolearn, and over-reliance on sub-optimal trajectories can make it hard for theagent to improve its performance. To solve these problems we propose a newalgorithm named TD3fG that can smoothly transition from learning from expertsto learning from experience. Our algorithm achieves good performance in theMUJOCO environment with limited and sub-optimal demonstrations. We use behaviorcloning to train the network as a reference action generator and utilize it interms of both loss function and exploration noise. This innovation can helpagents extract a priori knowledge from demonstrations while reducing thedetrimental effects of the poor Markovian properties of the demonstrations. Ithas a better performance compared to the BC+ fine-tuning and DDPGfD approach,especially when the demonstrations are relatively limited. We call our methodTD3fG meaning TD3 from a generator.

Improving Learning from Demonstrations by Learning from Experience