ASC我可以做任何事情：针对已培训的AI的多任务培训
ASC me to Do Anything: Multi-task Training for Embodied AI
摘要：
人工智能已经在各种独立任务中取得了稳步进展。虽然这些不同的任务有不同的最终目标，但成功完成它们所需的基本技能却有很大的重叠。在本文中，我们的目标是利用这些共享技能来学习联合执行多项任务。我们提出了原子技能完成（ASC），这是一种用于人工智能多任务训练的方法，其中多个任务之间共享的一组原子技能被组合在一起以执行任务。这一办法成功的关键是有一个预先培训计划，从高级别任务中学习技能，使联合培训有效。我们使用ASC在AI 2-THOR环境中训练代理，以联合执行四个交互式任务，并发现它非常有效。在多任务设置中，与没有预训练相比，ASC在可见场景上将成功率提高了2倍，在不可见场景上提高了4倍。重要的是，ASC使我们能够训练多任务代理，其成功率比训练4个独立的单任务代理高52%。最后，我们的分层代理比传统的黑盒架构更可解释。
英文摘要：
Embodied AI has seen steady progress across a diverse set of independent tasks. While these varied tasks have different end goals, the basic skills required to complete them successfully overlap significantly. In this paper, our goal is to leverage these shared skills to learn to perform multiple tasks jointly. We propose Atomic Skill Completion (ASC), an approach for multi-task training for Embodied AI, where a set of atomic skills shared across multiple tasks are composed together to perform the tasks. The key to the success of this approach is a pre-training scheme that decouples learning of the skills from the high-level tasks making joint training effective. We use ASC to train agents within the AI2-THOR environment to perform four interactive tasks jointly and find it to be remarkably effective. In a multi-task setting, ASC improves success rates by a factor of 2x on Seen scenes and 4x on Unseen scenes compared to no pre-training. Importantly, ASC enables us to train a multi-task agent that has a 52% higher Success Rate than training 4 independent single task agents. Finally, our hierarchical agents are more interpretable than traditional black-box architectures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
