On Mon, Jan 28, 2013 at 6:21 PM, Aaron Hosford <[email protected]> wrote:

> In regards to the idea that intrinsic rewards are somehow different from
> extrinsic ones, a reward signal can just as easily be modulated by internal
> events (thoughts) as external ones (percepts). Furthermore, if you read up
> on RL, you'll see that in all effective multi-step RL-style algorithms,
> there is a backward chaining of reward, so that previous behaviors or other
> early triggers for a behavior are rewarded, not just the immediate actions.
> All actions, whether extrinsically or intrinsically rewarding, derive their
> value from either immediate or indirect/backward-chained reward signals,
> which means we can modulate behavior arbitrarily to any level of complexity
> with relatively minimal difficulty by taking advantage of this backward
> chaining.
>
Well the fact that backwards chaining of the actions leading up to a
rewarded behavior is an interesting point. And while anyone with a little
imagination could come up with a creative means to develop a way to use RL
to reinforce complex behaviors based on parts of a behavior string that is
reinforced this is not explained by the backward-chained reward signals
that you mentioned.
But looking beyond that the claim that any internal motivation could be
explained by external reinforcement is unnecessarily complicated because it
is dependent on external rewards which would demand that things like the
massive levels of complexity of infinitesimal past rewards could explain
inner direction. This is the same problem as insisting that Bayesian
Reasoning along with some priors are all that is necessary to explain human
intelligence. Sorry but it just does not work - unless you change the
presumptions of what is meant by Reinforcement Learning or Bayesian
Reasoning. (Which is ok, I am just saying...)
Jim Bromer



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