rohinmshah's Shortform

post by rohinmshah · 2020-01-18T23:21:02.302Z · score: 14 (3 votes) · LW · GW · 2 comments


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comment by rohinmshah · 2020-01-18T23:21:02.473Z · score: 6 (3 votes) · LW(p) · GW(p)

I was reading Avoiding Side Effects By Considering Future Tasks, and it seemed like it was doing something very similar to relative reachability. This is an exploration of that; it assumes you have already read the paper and the relative reachability paper. It benefitted from discussion with Vika.

Define the reachability , where  is the optimal policy for getting from to , and is the length of the trajectory. This is the notion of reachability both in the original paper and the new one.

Then, for the new paper when using a baseline, the future task value is:

where is the baseline state and is the future goal.

In a deterministic environment, this can be rewritten as:

Here, is relative reachability, and the last line depends on the fact that the goal is equally likely to be any state.

Note that the first term only depends on the number of timesteps, since it only depends on the baseline state s'. So for a fixed time step, the first term is a constant.

The optimal value function in the new paper is (page 3, and using my notation of instead of their ):


This is the regular Bellman equation, but with the following augmented reward (here is the baseline state at time t):

Terminal states:

Non-terminal states:

For comparison, the original relative reachability reward is:

The first and third terms in are very similar to the two terms in . The second term in only depends on the baseline.

All of these rewards so far are for finite-horizon MDPs (at least, that's what it sounds like from the paper, and if not, they could be anyway). Let's convert them to infinite-horizon MDPs (which will make things simpler, though that's not obvious yet). To convert a finite-horizon MDP to an infinite-horizon MDP, you take all the terminal states, add a self-loop, and multiply the rewards in terminal states by a factor of (to account for the fact that the agent gets that reward infinitely often, rather than just once as in the original MDP). Also define for convenience. Then, we have:

Non-terminal states:

What used to be terminal states that are now self-loop states:

Note that all of the transformations I've done have preserved the optimal policy, so any conclusions about these reward functions apply to the original methods. We're ready for analysis. There are exactly two differences between relative reachability and future state rewards:

First, the future state rewards have an extra term, .

This term depends only on the baseline . For the starting state and inaction baselines, the policy cannot affect this term at all. As a result, this term does not affect the optimal policy and doesn't matter.

For the stepwise inaction baseline, this term certainly does influence the policy, but in a bad way: the agent is incentivized to interfere with the environment to preserve reachability. For example, in the human-eating-sushi environment, the agent is incentivized to take the sushi off of the belt, so that in future baseline states, it is possible to reach goals that involve sushi.

Second, in non-terminal states, relative reachability weights the penalty by instead of . Really since and thus is an arbitrary hyperparameter, the actual big deal is that in relative reachability, the weight on the penalty switches from in non-terminal states to the smaller in terminal / self-loop states. This effectively means that relative reachability provides an incentive to finish the task faster, so that the penalty weight goes down faster. (This is also clear from the original paper: since it's a finite-horizon MDP, the faster you end the episode, the less penalty you accrue over time.)

Summary: The actual effects of the new paper's framing 1. removes the "extra" incentive to finish the task quickly that relative reachability provided and 2. adds an extra reward term that does nothing for starting state and inaction baselines but provides an interference incentive for the stepwise inaction baseline.

(That said, it starts from a very different place than the original RR paper, so it's interesting that they somewhat converge here.)

comment by rohinmshah · 2020-01-23T23:51:50.345Z · score: 4 (2 votes) · LW(p) · GW(p)

In my double descent newsletter [AF · GW], I said:

This fits into the broader story being told in other papers that what's happening is that the data has noise and/or misspecification, and at the interpolation threshold it fits the noise in a way that doesn't generalize, and after the interpolation threshold it fits the noise in a way that does generalize. [...]

This explanation seems like it could explain double descent on model size and double descent on dataset size, but I don't see how it would explain double descent on training time. This would imply that gradient descent on neural nets first has to memorize noise in one particular way, and then further training "fixes" the weights to memorize noise in a different way that generalizes better. While I can't rule it out, this seems rather implausible to me. (Note that regularization is not such an explanation, because regularization applies throughout training, and doesn't "come into effect" after the interpolation threshold.)

One response you could have is to think that this could apply even at training time, because typical loss functions like cross-entropy loss and squared error loss very strongly penalize confident mistakes, and so initially the optimization is concerned with getting everything right, only later can it be concerned with regularization.

I don't buy this argument either. I definitely agree that cross-entropy loss penalizes confident mistakes very highly, and has a very high derivative, and so initially in training most of the gradient will be reducing confident mistakes. However, you can get out of this regime simply by predicting the frequencies of each class (e.g. uniform for MNIST). If there are N classes, the worst case loss is when the classes are all equally likely, in which case the average loss per data point is when (as for CIFAR-10, which is what their experiments were done on), which is not a good loss value but it does seem like regularization should already start having an effect. This is a really stupid and simple classifier to learn, and we'd expect that the neural net does at least this well very early in training, well before it reaches the interpolation threshold / critical regime, which is where it gets ~perfect training accuracy.

There is a much stronger argument in the case of L2 regularization on MLPs and CNNs with relu activations. Presumably, if the problem is that the cross-entropy "overwhelms" the regularization initially, then we should also see double descent if we first train only on cross-entropy, and then train with L2 regularization. However, this can't be true. When training on just L2 regularization, the gradient descent update is:

for some constant .

For MLPs and CNNs with relu activations, if you multiply all the weights by a constant, the logits also get multiplied by a constant, no matter what the input is. This means that the train/test error cannot be affected by L2 regularization alone, and so you can't see a double descent on test error in this setting. (This doesn't eliminate the possibility of double descent on test loss, since a change in the magnitude of the logits does affect the cross-entropy, but the OpenAI paper shows double descent on test error as well, and that provably can't happen in the "first train to zero error with cross-entropy and then regularize" setting.)

The paper tests with CNNs, but doesn't mention what activation they use. Still, I'd find it very surprising if double descent only happened for a particular activation function.