Right, in the limit of infinite training, this approach will learn exactly what we want it to learn. Theoretical sufficiency is not generally of interest to engineers trying to build systems that solve real problems.
Reinforcement learning is a good tool for learning something, when *all you have* is a value function. It is, however, only a small part of our ways to have computers learn to be intelligent. For example, how do you learn to not get hit by a car? Hopefully your AI will have something better than *only* reinforcement learning. Bo On Sat, 23 Jun 2007, Philip Goetz wrote: ) On 6/22/07, Bo Morgan <[EMAIL PROTECTED]> wrote: ) > ) > You make AGI sound like a "members only" club by this "obligatory" ) > comment. ;) ) > ) > Reinforcement learning is a simple theory that only solves problems for ) > which we can design value functions. ) ) Can you explain what you mean by a value function? ) If "success" or "failure" are sufficient value functions (and I think ) they are), then this covers a wide class of problems. ) ) ----- ) This list is sponsored by AGIRI: http://www.agiri.org/email ) To unsubscribe or change your options, please go to: ) http://v2.listbox.com/member/?&; ) ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415&user_secret=e9e40a7e
