On Mon, May 9, 2022, 8:15 AM Undiscussed Horrific Abuse, One Victim of Many <gmkarl@gmail.com> wrote:
On Mon, May 9, 2022, 8:14 AM Undiscussed Horrific Abuse, One Victim of Many <gmkarl@gmail.com> wrote:
On Mon, May 9, 2022, 8:12 AM Undiscussed Horrific Abuse, One Victim of Many <gmkarl@gmail.com> wrote:
On Mon, May 9, 2022, 8:05 AM Undiscussed Horrific Abuse, One Victim of Many <gmkarl@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.
>>>> >>> But something different is thinking at this time. It is the role of a
provided for in these libraries. 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].
in the end, what is important is what you are asking to change in the
real world. if the final goal state has an infinite quantity, then maximisation has been misused [still thinking though, this leaked out]