论文标题
了解重要的事情：跨域模仿学习与任务相关的嵌入
Learn what matters: cross-domain imitation learning with task-relevant embeddings
论文作者
论文摘要
我们研究自主代理如何学会从不同领域（例如不同环境或不同代理）中的示范中执行任务。这样的跨域模仿学习是需要从人类专家的演示中培训人造代理的。我们提出了一个可扩展的框架，该框架可以实现跨域模仿学习，而无需访问其他演示或其他领域知识。我们共同培训学习者的政策，并通过对抗性培训学习学习者和专家领域的映射。我们通过使用共同信息标准来找到包含与任务相关的信息的专家状态空间的嵌入，并且是域细节不变的。此步骤大大简化了估计学习者与专家领域之间的映射，从而有助于端到端学习。我们证明了在相当不同的域之间成功转移了政策，而没有额外的监督，例如其他示范，以及其他方法失败的情况。
We study how an autonomous agent learns to perform a task from demonstrations in a different domain, such as a different environment or different agent. Such cross-domain imitation learning is required to, for example, train an artificial agent from demonstrations of a human expert. We propose a scalable framework that enables cross-domain imitation learning without access to additional demonstrations or further domain knowledge. We jointly train the learner agent's policy and learn a mapping between the learner and expert domains with adversarial training. We effect this by using a mutual information criterion to find an embedding of the expert's state space that contains task-relevant information and is invariant to domain specifics. This step significantly simplifies estimating the mapping between the learner and expert domains and hence facilitates end-to-end learning. We demonstrate successful transfer of policies between considerably different domains, without extra supervision such as additional demonstrations, and in situations where other methods fail.
