Intelligent Agent Foundations Forumsign up / log in
Value Learning for Irrational Toy Models
discussion post by Patrick LaVictoire 191 days ago | discuss

(This is a half-formed idea from discussions within MIRI; if it’s dumb, I take the full blame.)

In value learning contexts, it’s useful to have a toy model of human psychology, to see (for example) if a certain approach would work to learn the values of an idealized rational agent, but might robustly fail when faced with a more realistically constructed agent.

For example, here is a toy model of an irrational agent: take a world where the deterministic mapping \(f\) from actions to outcomes is fully known, and take three different preference orderings on the set of outcomes. When the agent chooses among actions \(a_1, \dots, a_n\), we first check whether any \(f(a_i)\) dominates the others according to at least two of the preference orderings; if so, we take that action with certainty. Otherwise, we select randomly among the available actions. (This agent is a moral democracy, and if two of the three subagents agree on a policy, that policy is taken; otherwise, the agent hits a deadlock and acts at random.)

It is easy to construct agents of this form which exhibit circular preferences in binary choices. We can therefore ask whether a particular value learning algorithm would satisfy sensible desiderata when learning from such an agent. (For instance, if outcome \(X\) strictly dominates outcome \(Y\) according to all three preference orderings, we might desire that our value learning algorithm not act so as to result in \(Y\) when it could instead have acted so as to result in \(X\).)

The Hard Problem of Value Learning

But of course, a human brain is not even as simple as that toy model of irrationality. I’ve thought it might be useful to sketch out the level of generality that I actually believe is necessary, in order to show how hard the problem may be to get right.

Human brains do some amount of consequentialist reasoning [citation needed], so arguably at some point of cognition there exist heuristics for evaluating the overall desirability of various outcomes. We would like our value learning process to infer these heuristics and take them into account (this seems necessary, not sufficient).

We cannot assume that the human will take actions that effectively argmax these heuristics (though it will strongly correlate in some regime); we cannot assume whether these heuristics give us values for states, or for action-state pairs; we cannot assume that these heuristics make use of all the important information from the original observations, etc.

It seems to me as if our value learning algorithm will be trying to figure out the contents of the red box from the blue boxes:

Value Learning Diagram
This is not as hopeless as it seems, since we still have an assumption that the mapping from observations to actions approximately factors in this way, and that the orange boxes have been at least somewhat selected for performance. But it’s a far cry beyond what CIRL, for example, would be able to infer.



NEW LINKS

NEW POSTS

NEW DISCUSSION POSTS

RECENT COMMENTS

Indeed there is some kind of
by Vadim Kosoy on Catastrophe Mitigation Using DRL | 0 likes

Very nice. I wonder whether
by Vadim Kosoy on Hyperreal Brouwer | 0 likes

Freezing the reward seems
by Vadim Kosoy on Resolving human inconsistency in a simple model | 0 likes

Unfortunately, it's not just
by Vadim Kosoy on Catastrophe Mitigation Using DRL | 0 likes

>We can solve the problem in
by Wei Dai on The Happy Dance Problem | 1 like

Maybe it's just my browser,
by Gordon Worley III on Catastrophe Mitigation Using DRL | 2 likes

At present, I think the main
by Abram Demski on Looking for Recommendations RE UDT vs. bounded com... | 0 likes

In the first round I'm
by Paul Christiano on Funding opportunity for AI alignment research | 0 likes

Fine with it being shared
by Paul Christiano on Funding opportunity for AI alignment research | 0 likes

I think the point I was
by Abram Demski on Predictable Exploration | 0 likes

(also x-posted from
by Sören Mindermann on The Three Levels of Goodhart's Curse | 0 likes

(x-posted from Arbital ==>
by Sören Mindermann on The Three Levels of Goodhart's Curse | 0 likes

>If the other players can see
by Stuart Armstrong on Predictable Exploration | 0 likes

Thinking about this more, I
by Abram Demski on Predictable Exploration | 0 likes

> So I wound up with
by Abram Demski on Predictable Exploration | 0 likes

RSS

Privacy & Terms