The machines gain possibly different utilities by processing different jobs, and all jobs assigned to the same machine should be processed without overlap.

In this paper, we consider the problem of iterative machine teaching, where a teacher provides examples sequentially based on the current iterative learner.

ECRT consistently outperforms the others, especially in the low-sample regime.

The robot could learn from these demonstrations to complete the tasks autonomously. For IL algorithms to be useful, however, they must be able to learn how to perform tasks from few demonstrations. A domestic robot wouldn't be very helpful if it required thirty demonstrations before it figured out that you are deliberately washing your purple cravat

We visualize the domain adaptation performance on the transfer scenario NIH-CXR14, CheXpert, MIMIC-CXR to Open-i . The visualization sample in the Open-i is suffering from Atelecsis and Effusion disease.

Encourage controller to search unseen blocks by Eq. 9 Get reward r by Eq. 3 We conduct an ablation study to show the strength of count-based search exploration. We compare the performance difference between InstAParam with and without count-based exploration. Although, InstaNAS tries to solve the problem with "policy shuffling", we found that it does not solve the problem in this scenario. The detailed accuracy is listed in Table 2. CIFAR-10 and does not sacrifice the initial performance. First, we will focus on the distribution of the policy for each task.

Reinforcement learning (RL) promises to enable autonomous acquisition of complex behaviors for diverse agents.