A universal score for optimizers

by levin 2 min read10th Jul 20188 comments


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Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.

[Epistemic status: the idea is simple and intuitive, so I wouldn't be surprised if it has appeared before. If so, I would appreciate pointers.]

Eliezer defines optimization power as the ability to produce solutions higher on the preference ordering. This feels right, but it would be nice to convert this intuition into a score that can be measured, a universal metric that allows one to meaningfully state that AlphaZero is better at Go than Deep Blue was at chess. In this post I try accomplishing just that. I start by giving a candidate metric and some of its properties. I then discuss how it can be approximately computed in complex settings, and describe a somewhat paradoxical implication of adopting this definition for repeated games. 

For simplicity I will assume a deterministic, finite-action-space environment (extending everything to MDPs with uncountable actions doesn't seem to be a fundamental obstacle). The action space is , the outcome space is , utility of an agent under question is . Because is deterministic I will just write for brevity. Note that we can represent sequential decision problems in this framework (e.g. Sudoku), elements of A would then be vectors of individual actions. We write n(S) for the size of set S.

Definition. Suppose we observe an agent with utility function u(a) take an action , then we say this agent has a C-score of:

Intuitively, C-score is inversely proportional to the fraction of outcomes that are as good for the agent as the one it managed to obtain.  One interpretation of this formula is that the agent is competing against a bot that just picks a random action (random-bot), and then is log probability of losing in this competition. 

Some properties of C(u,a):

  • C-score is independent of computational power employed
  • It's impossible to compute C-score without knowing the utility function
  • Improving from being in top 1% outcomes to top 0.1% is as "hard" as improving from 10% to 1%  
  • One can use Monte-Carlo to generate lower bounds of the form "w.h.p. C-score of this agent is at least Y" 
  • Strategy of taking a random action produces the same score in any setting (under some assumptions it might also be approximately true for the strategy of trying k random moves and picking the best one)

Approximating C-score

Can we estimate C-score for complex environments and agents? For example, suppose we have an agent playing chess against a known distribution of opponents (or, more simply, against random-bot), and utility function is the probability of winning the game (action space then is a set of policies, i.e. mappings from state to action). Can we measure C-score of this agent without using an unrealistic amount of compute?

Clearly, the naive Monte-Carlo approach I mentioned above is a no-go in this scenario: the probability that a randomly sampled action would be in the set is tiny (if the agent is any good), and we will never sample this set.

I have a couple of potential ideas here:

  • In case of having access to a better optimizer than the one being measured,  one can use rare event sampling, that is bias the sampling procedure towards good policies and then account for this bias when computing the C-score. This approach nicely fits with the intuition "you need to be at least about as smart as the agent to measure how smart it is"
  • If the game is DP/MDP and the agent employs value function estimation for picking moves, one can try looking at how much the agent improves when enhanced with MCTS, and then try inferring C-score from it. I don't have good explanations for why and how this can work, only an intuition that this is a meaningful approach to try.

Repeated game paradox

I'll use a very simple game to illustrate this issue. An agent picks a number between 1 and 10 and utility of the agent equals to the number chosen. This game is repeated T times, so agent's total utility is the sum of numbers it returned in all T games. 

If T=1 and the agent picks 8, we have (there are 3/10 actions that yield utility of at least 8)

If T=2 and the agent picks 8 twice, we get


Thus an agent that finds a good solution once at t=1, and then repeats the same action until the end, obtains higher and higher scores for bigger T. This feels like an artifact of the definition (coming from how volumes grow with dimensions), rather than the agent being a genuinely better optimizer. Is there a score formula that has similar properties but doesn't suffer from this paradox?

Thanks to Linda, Joar and Chris for helpful discussions that led to this post.


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