Re:Learning to reinforcement learn
by zhongwenxu on Sunday November 20, 2016 @08:35PM
(#13002418)
On Google and Oxford's cases, I don't think it is common to broadcast among your colleagues in different groups your proposed ideas before you actually go into it. So I think it may happen even in the same company/university.
"Our work draws inspiration from a particular line of work (Younger et al.,
2001; Santoro et al., 2016; Vinyals et al., 2016), which formulates meta-learning as an optimization
problem, and can thus be optimized end-to-end via gradient descent."
"Another line of work (Hochreiter et al., 2001;
Younger et al., 2001; Andrychowicz et al., 2016; Li & Malik, 2016) studies meta-learning over the
optimization process. There, the meta-learner makes explicit updates to a parametrized model."
Inspired by the same works, apply the meta learning idea into RL problems, meet the ICLR deadline together. Still make sense right?
For the meta-learning papers, you may have interests to read the related work part of the RL^2 paper https://arxiv.org/pdf/1611.02779.pdf.
Quoted as follows,
"Our work draws inspiration from a particular line of work (Younger et al., 2001; Santoro et al., 2016; Vinyals et al., 2016), which formulates meta-learning as an optimization problem, and can thus be optimized end-to-end via gradient descent."
"Another line of work (Hochreiter et al., 2001; Younger et al., 2001; Andrychowicz et al., 2016; Li & Malik, 2016) studies meta-learning over the optimization process. There, the meta-learner makes explicit updates to a parametrized model."
Inspired by the same works, apply the meta learning idea into RL problems, meet the ICLR deadline together. Still make sense right?