The Efficient Learning of Multiple Task Sequences

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning (2017, background): “This makes it hard to evaluate the agent’s ability to compose pre-learned policies to solve previously unseen sequential tasks in a zero-shot fashion unless we re-train the agent on the new tasks in a transfer learning setting (Singh, 1991; 1992; McGovern and Barto, 2002).”
Balancing Multiple Sources of Reward in Reinforcement Learning (2000, background): “Work in multiple goals (see [3, 8] as examples) is also related but assumes either that the returns of the goals are to be linearly combined for an overall value function or that only one goal is to be solved at a time.”
Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning (1999, result): “Although our formalism is slightly different, these results are in essence taken or adapted from prior work (including classical SMDP work and [5,44,52–57,65–68,71,74, 75]).”
HQ-Learning (1997, methodology): “Jaakkola, T., Singh, S. P., and Jordan, M. I. (1995).”