Online Multi-Task Learning Using Active Sampling

Sahil Sharma, Balaraman Ravindran

Feb 17, 2017 (modified: Feb 22, 2017) ICLR 2017 workshop submission readers: everyone
  • Abstract: One of the long-standing challenges in Artificial Intelligence for goal-directed behavior is to build a single agent which can solve multiple tasks. Recent progress in multi-task learning for goal-directed sequential tasks has been in the form of distillation based learning wherein a single student network learns from multiple task-specific expert networks by mimicking the task-specific policies of the expert networks. While such approaches offer a promising solution to the multi-task learning problem, they require supervision from large task-specific (expert) networks which require extensive training. We propose a simple yet efficient multi-task learning framework which solves multiple goal-directed tasks in an online or active learning setup without the need for expert supervision.
  • TL;DR: A novel online multi-task reinforcement learning approach which does not require expert supervision
  • Conflicts:
  • Keywords: Deep learning, Reinforcement Learning