[ot][spam][crazy] draft: learning RL

[Undiscussed Horrific Abuse, One Victim of Many]

On Mon, May 9, 2022, 8:14 AM Undiscussed Horrific Abuse, One Victim of Many
<gmkarl at gmail.com> wrote:

>
>
> On Mon, May 9, 2022, 8:12 AM Undiscussed Horrific Abuse, One Victim of
> Many <gmkarl at gmail.com> wrote:
>
>>
>>
>> On Mon, May 9, 2022, 8:05 AM Undiscussed Horrific Abuse, One Victim of
>> Many <gmkarl at gmail.com> wrote:
>>
>>> To represent normal goal behavior with maximization, the
>>>>>>>>>
>>>>>>>>
>> This is all confused to me, but normally when we meet goals we don't
>> influence things not related to the goal. This is not usually included in
>> maximization, unless
>>
>> return function needs to not only be incredibly complex, but
>>>>>>>>>
>>>>>>>>
>> the return to be maximized were to include them, by maybe always being
>> 1.0, I don't really know.
>>
>> also feed back to its own evaluation, in a way not
>>>>>>>>>
>>>>>>>>
>> Maybe this relates to not learning habits unrelated to the goal, that
>> would influence other goals badly.
>>
>> provided for in these libraries.
>>>>>>>>>
>>>>>>>>
>> But something different is thinking at this time. It is the role of a
>> part of a mind to try to relate with the other parts. Improving this in a
>> general way is likely known well to be important.
>>
>>
>>> Daydreaming: I'm thinking of how in reality and normality, we have many
>>> many goals going at once (most of them "common sense" and/or "staying being
>>> a living human").  Similarly, I'm thinking of how with normal transformer
>>> models, one trains according to a loss rather than a reward.
>>>
>>> I'm considering what if it were more interesting when an agent _fails_
>>> to meet a goal. Its reward would usually be full, 1.0, but would multiply
>>> by losses when goals are not met.
>>>
>>> This seems much nicer to me.
>>>
>>
> I don't know how RL works since I haven't taken the course, but it looks
> to me from a distance like it would just learn at a different (slower) rate
> [with other differences]
> > yes
>
> I think it relates to the other inhibited concept, of value vs action
learning. a reward starts at just the event of interest, for example, but
the system then learns to apply rewards to things that can relate to the
event, like preceding time points [states].

>
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