Sample inefficiency of deep reinforcement learning methods is a major obstacle for their use in real-world applications. In this work, we show how human demonstrations can improve final performance of agents on the Minecraft minigame ObtainDiamond with only 8M frames of environment interaction. We propose a training procedure where policy networks are first trained on human data and later fine-tuned by reinforcement learning. Using a policy exploitation mechanism, experience replay and an additional loss against catastrophic forgetting, our best agent was able to achieve a mean score of 48. Our proposed solution placed 3rd in the NeurIPS MineRL Competition for Sample-Efficient Reinforcement Learning.
@article{arxiv.2003.06066,
title = {Sample Efficient Reinforcement Learning through Learning from Demonstrations in Minecraft},
author = {Christian Scheller and Yanick Schraner and Manfred Vogel},
journal= {arXiv preprint arXiv:2003.06066},
year = {2020}
}