[Robot-learning] concepts in deep learning networks

[Littman, Michael]

That's helpful, thanks, Nakul!

I tracked down the video:

https://www.youtube.com/watch?v=UWl70BMsgfg

It's nice to see that they finally solved the problem of integrating RL
control with in 70s action movie music.


On Wed, Sep 27, 2017 at 6:47 AM, Nakul Gopalan <nakul_gopalan at brown.edu>
wrote:

> The work seems to be a combination of AMDPs and the DAQN work that Mel and
> Chris are working on. An end to end task is broken into subtasks, each with
> an independent reward function and state-action space. They are learning
> each subtask independently and not all at the same time like DAQN. They
> chose a domain that Deepmind did for stacking blocks and showed that a
> policy for stack blocks can be learned 45x faster. Their video page does
> not exist so I can't really see their robot in action.
> The paper itself lacks a lot of related work, they only cite the early
> options work, ignore everything after that and criticise two papers from
> last year. They do not point out that the hierarchy looks like a MAXQ
> hierarchy and do not talk about optimality issues of such an approach.
> Best
> nakul
>
>
> On 26 September 2017 at 15:00, Littman, Michael <mlittman at cs.brown.edu>
> wrote:
>
>> indeed... has anyone looked over the paper? any insights?
>>
>> On Tue, Sep 26, 2017 at 2:48 PM, Marie desJardins <mariedj at umbc.edu>
>> wrote:
>>
>>> Looks an awful lot like AMDPs...
>>>
>>> https://www.forbes.com/sites/aarontilley/2017/09/19/ai-start
>>> up-invents-trick-for-robots-to-more-efficiently-teach-themse
>>> lves-complex-tasks/#5a6254d315fe
>>>
>>> https://drive.google.com/file/d/0B7-VbSZ5FzXBdURHLXV4OU9EOTQ/view
>>>
>>> Marie
>>>
>>> --
>>> Dr. Marie desJardins
>>> Associate Dean for Academic Affairs
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