标题： 通过在Minecraft中从示范中学习实现高效的强化学习 显示英文标题

标题： Sample Efficient Reinforcement Learning through Learning from Demonstrations in Minecraft

摘要： 深度强化学习方法的样本效率低下是其在现实世界应用中的主要障碍。 在本工作中，我们展示了如何通过人类演示来提高代理在Minecraft小游戏ObtainDiamond中的最终性能，仅需800万帧的环境交互。 我们提出了一种训练过程，其中策略网络首先在人类数据上进行训练，然后通过强化学习进行微调。 使用策略利用机制、经验回放和对抗灾难性遗忘的额外损失，我们的最佳代理能够达到平均分数48。 我们提出解决方案在NeurIPS MineRL竞赛中获得了样本高效强化学习的第三名。 显示英文摘要

摘要： 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.