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All of Zachary Robertson's Comments + Replies

MDP models are determined by the agent architecture and the environmental dynamics
Repetitive Experimenter1y-3

I don’t think it’s a good use of time to get into this if you weren’t being specific about your usage of ‘model’ or the claim you made previously because I already pointed out a concrete difference: I claim it’s reasonable to say there are three alternatives while you claim there are two alternatives.

(If it helps you, you can search-replace model-irrelevant to state-abstraction because I don’t use the term model in my previous reply anyway.)

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8Alex Turner1y
I don't understand your point in this exchange. I was being specific about my usage of model; I meant what I said in the original post, although I noted room for potential confusion in my comment [https://www.lesswrong.com/posts/XkXL96H6GknCbT5QH/mdp-models-are-determined-by-the-agent-architecture-and-the?commentId=yRBM5BJoRHmtbBD9o] above. However, I don't know how you're using the word. You used the word 'model' in both of your prior comments, and so the search-replace yields "state-abstraction-irrelevant abstractions." Presumably not what you meant? That's not a "concrete difference." I don't know what you mean when you talk about this "third alternative." You think you have some knockdown argument - that much is clear - but it seems to me like you're talking about a different consideration entirely. I likewise feel an urge to disengage, but if you're interested in explaining your idea at some point, message me and we can set up a higher-bandwidth call.
MDP models are determined by the agent architecture and the environmental dynamics
Repetitive Experimenter1y-1

This was why gave a precise definition of model-irrelevance. I'll step through your points using the definition,

  1. Consider the underlying environment (assumed Markovian)
  2. Consider different state/action encodings (model-irrelevant abstractions) we might supply the agent.
  3. For each, fix a reward function distribution
  4. See what the theory predict

The problem I'm trying to highlight lies in point three. Each task is a reward function you could have the agent attempt to optimize. Every abstraction/encoding fixes a set of rewards under which the abstraction is mo... (read more)

Reply
8Alex Turner1y
I read your formalism, but I didn't understand what prompted you to write it. I don't yet see the connection to my claims. Yeah, I don't want you to spend too much time on a bulletproof grounding of your argument, because I'm not yet convinced we're talking about the same thing. In particular, if the argument's like, "we usually express reward functions in some featurized or abstracted way, and it's not clear how the abstraction will interact with your theorems" / "we often use different abstractions to express different task objectives", then that's something I've been thinking about but not what I'm covering here. I'm not considering practical expressibility issues over the encoded MDP: ("That's also a claim that we can, in theory, specify reward functions which distinguish between 5 googolplex variants of red-ghost-game-over.") If this doesn't answer your objection - can you give me an english description of a situation where the objection holds? (Let's taboo 'model', because it's overloaded in this context)
MDP models are determined by the agent architecture and the environmental dynamics
Repetitive Experimenter1y-1

I still see room for reasonable objection.

An MDP model (technically, a rewardless MDP) is a tuple

I need to be pedantic. The equivocation here is where I think the problem is. To assign a reward function we need a map from the state-action space to the reals. It's not enough to just consider a 'rewardless MDP'.

When we define state and action encodings, this implicitly defines an "interface" between the agent and the environment.

As you note, the choice of state-action encoding is an implicit modeling assumption. It could be wrong, but to even... (read more)

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8Alex Turner1y
Why would we need that, and what is the motivation for "models"? The moment we give the agent sensors and actions, we're done specifying the rewardless MDP (and its model). ETA: potential confusion - in some MDP theory, the “model” is a model of the environment dynamics. Eg in deterministic environments, the model is shown with a directed graph. i don’t use “model” to refer to an agent’s world model over which it may have an objective function. I should have chosen a better word, or clarified the distinction. If, by "tasks", you mean "different agent deployment scenarios" - I'm not claiming that. I'm saying that if we want to predict what happens, we: 1. Consider the underlying environment (assumed Markovian) 2. Consider different state/action encodings we might supply the agent. 3. For each, fix a reward function distribution (what goals we expect to assign to the agent) 4. See what the theory predicts. There's a further claim (which seems plausible, but which I'm not yet making) that (2) won't affect (4) very much in practice. The point of this post is that if you say "the MDP has a different model", you're either disagreeing with (1) the actual dynamics, or claiming that we will physically supply the agent with a different state/action encoding (2). I don't follow. Can you give a concrete example?
Why Neural Networks Generalise, and Why They Are (Kind of) Bayesian
Repetitive Experimenter1y1

The main point, as I see it, is essentially that functions with good generalisation correspond to large volumes in parameter-space, and that SGD finds functions with a probability roughly proportional to their volume.

What I'm suggesting is that volume in high-dimensions can concentrate on the boundary. To be clear, when I say SGD only typically reaches the boundary, I'm talking about early stopping and the main experimental setup in your paper where training is stopped upon reaching zero train error.

We have done overtraining, which should allow SGD to

... (read more)
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2Joar Skalse1y
Yes. I imagine this is why overtraining doesn't make a huge difference. See e.g., page 47 in the main paper [https://arxiv.org/pdf/2006.15191.pdf].
Developmental Stages of GPTs
Repetitive Experimenter2y2

The paper doesn't draw the causal diagram "Power → instrumental convergence", it gives sufficient conditions for power-seeking being instrumentally convergent. Cycle reachability preservation is one of those conditions.

This definitely feels like the place where I'm missing something. What is the formal definition of 'power seeking'? My understanding is that power is the rescaled value function, in the limit of farsightedness is decreasing, and in the context of terminal state reachability always goes to zero. The agent literally gives up power to achiev... (read more)

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2Alex Turner1y
The freshly updated paper [https://arxiv.org/pdf/1912.01683.pdf] answers this question in great detail; see section 6 and also appendix B.
1Alex Turner2y
Great question. One thing you could say is that an action is power-seeking compared to another, if your expected (non-dominated subgraph; see Figure 19) power is greater for that action than for the other. Power is kinda weird when defined for optimal agents, as you say - whenγ=1, POWER can only decrease. See Power as Easily Exploitable Opportunities [https://www.lesswrong.com/posts/eqov4SEYEbeFMXegR/power-as-easily-exploitable-opportunities] for more on this. Shortly after Theorem 19, the paper says: "In appendix C.6.2, we extend this reasoning to k-cycles (k >1) via theorem 53 and explain how theorem19 correctly handles fig. 7". In particular, see Figure 19. The key insight is that Theorem 19 talks about how many agents end up in a set of terminal states, not how many go through a state to get there. If you have two states with disjoint reachable terminal state sets, you can reason about the phenomenon pretty easily. Practically speaking, this should often suffice: for example, the off-switch state is disjoint from everything else. If not, you can sometimes consider the non-dominated subgraph in order to regain disjointness. This isn't in the main part of the paper, but basically you toss out transitions which aren't part of a trajectory which is strictly optimal for some reward function. Figure 19 gives an example of this. The main idea, though, is that you're reasoning about what the agent's end goals tend to be, and then say "it's going to pursue some way of getting there with much higher probability, compared to this small set of terminal states (ie shutdown)". Theorem 17 tells us that in the limit, cycle reachability totally controls POWER. I think I still haven't clearly communicated all my mental models here, but I figured I'd write a reply now while I update the paper. Thank you for these comments, by the way. You're pointing out important underspecifications. :) I think one problem is that power-seeking agents are generally not that corrigible, w
Developmental Stages of GPTs
Repetitive Experimenter2y2

Thanks for the comment! I think max-ent brings up a related point. In IRL we observed behavior and infer a reward function (using max-ent also?). Ultimately, there is a relationship between state/action frequency and reward. This would considerably constrain the distribution of reward functions to be considered in instrumental/power analysis.

I think I get confused about the usage of power the most. It seems like you can argue that given a random reward to optimize the agent will try to avoid getting turned off without invoking power. If there's a collectio... (read more)

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2Alex Turner2y
To clarify, I don't assume that. The terminal states, even those representing the off-switch, also have their reward drawn from the same distribution. When you distribute reward IID over states, the off-state is in fact optimal for some low-measure subset of reward functions. But, maybe you're saying "for realistic distributions, the agent won't get any reward for being shut off and thereforeπ∗won't ever let itself be shut off". I agree, and this kind of reasoning is captured by Theorem 3 of Generalizing the Power-Seeking Theorems [https://www.lesswrong.com/posts/nyDnLif4cjeRe9DSv/generalizing-the-power-seeking-theorems#Reward_distribution_generalization] . The problem is that this is just a narrow example of the more general phenomenon. What if we add transient "obedience" rewards, what then? For some level of farsightedness (γclose enough to 1), the agent will still disobey, and simultaneously disobedience gives it more control over the future. The paper doesn't draw the causal diagram "Power→instrumental convergence", it gives sufficient conditions for power-seeking being instrumentally convergent. Cycle reachability preservation is one of those conditions. Yes, right. The point isn't that alignment is impossible, but that you have to hit a low-measure set of goals which will give you aligned or non-power-seeking behavior. The paper helps motivate why alignment is generically hard and catastrophic if you fail. Yes, ifr=h, introduce the agent. You can formalize a kind of "alignment capability" by introducing a joint distribution over the human's goals and the induced agent goals (preliminary Overleaf notes [https://www.overleaf.com/read/jjfmwmymtvtg]). So, if we had goal X, we'd implement an agent with goal X', and so on. You then take our expected optimal value under this distribution and find whether you're good at alignment, or whether you're bad and you'll build agents whose optimal policies tend to obstruct you. The doubling depends on the environment st
Developmental Stages of GPTs
Repetitive Experimenter2y0

I think this is a slight misunderstanding of the theory in the paper.

I disagree. What I'm trying to do is outline a reinterpretation of the 'power seeking' claim. I'm citing the pre-task section and theorem 17 to insist that power-seeking can only really happen in the pre-task because,

The way the theory does this is by saying that first a reward function is drawn from the distribution, then it is given to the agent, then the agent thinks really hard, and then the agent executes the optimal policy.

The agent is done optimizing before the main portion ... (read more)

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3Rohin Shah2y
I do think this is mostly a matter of translation of math to English being hard. Like, when Alex says "optimal agents seek power", I think you should translate it as "when we don't know what goal an optimal agent has, we should assign higher probability that it will go to states that have higher power", even though the agent itself is not thinking "ah, this state is powerful, I'll go there".
Developmental Stages of GPTs
Repetitive Experimenter2y1

I appreciate the more concrete definition of IC presented here. However, I have an interpretation that is a bit different from you. I'm following the formal presentation.

My base understanding is that a cycle with max average reward is optimal. This is essentially just a definition. In the case the agent doesn't know the reward function, it seems clear that the agent ought to position it's self in a state which gives it access to as many of these cycles as possible.

In your paper, theorem 19 suggests that given a choice between two sets of 1-cycles and ... (read more)

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1Alex Turner2y
Great observation. Similarly, a hypothesis called "Maximum Causal Entropy" once claimed that physical systems involving intelligent actors tended tended towards states where the future could be specialized towards many different final states, and that maybe this was even part of what intelligence was. However, people objected: (monogamous) individuals don't perpetually maximize their potential partners -- they actually pick a partner, eventually. My position on the issue is: most agents steer towards states which afford them greater power, and sometimes most agents give up that power to achieve their specialized goals. The point, however, is that they end up in the high-power states at some point in time along their optimal trajectory. I imagine that this is sufficient for the catastrophic power-stealing incentives: the AI only has to disempower us once for things to go irreversibly wrong.
3Rohin Shah2y
I think this is a slight misunderstanding of the theory in the paper. I'd translate the theory of the paper to English as: Any time the paper talks about "distributions" over reward functions, it's talking from our perspective. The way the theory does this is by saying that first a reward function is drawn from the distribution, then it is given to the agent, then the agent thinks really hard, and then the agent executes the optimal policy. All of the theoretical analysis in the paper is done "before" the reward function is drawn, but there is no step where the agent is doing optimization but doesn't know its reward. I'd rewrite this as: