【文件属性】：
文件名称：DeepMind 关系型深度强化学习 Relational Deep Reinforcement Learning
文件大小：6.81MB
文件格式：PDF
更新时间：2022-04-23 07:06:25
深度强化学习 人工智能
Relational Deep Reinforcement Learning--We introduce an approach for deep reinforcement learning (RL) that improves upon the
efficiency, generalization capacity, and interpretability of conventional approaches through
structured perception and relational reasoning. It uses self-attention to iteratively reason about
the relations between entities in a scene and to guide a model-free policy. Our results show that
in a novel navigation and planning task called Box-World, our agent finds interpretable solutions
that improve upon baselines in terms of sample complexity, ability to generalize to more complex
scenes than experienced during training, and overall performance. In the StarCraft II Learning
Environment, our agent achieves state-of-the-art performance on six mini-games – surpassing
human grandmaster performance on four. By considering architectural inductive biases, our
work opens new directions for overcoming important, but stubborn, challenges in deep RL.