Part of Estimate subplan weight.
Add a set of goals/ideas to a “future” git graph. In RL, authors often refer to this process as “action generation” (actions being analogous to ideas), with the action generator often being the policy. See for example:
We don’t use the term Exploration as Wikipedia does. See also:
Pick a direction#
Consider VNTE in widely different parts of the network to start, trying to avoid putting too much time into variations on the same VNTE (see Set wide goal). Once you’ve identified the parts of the network with high weight tasks, get into more focused planning work in that area of the network (effectively prune actions to explore the rest of the network). At this point, think beyond the “gold” you’ve already found with Set deep goal. Said another way, look for low hanging fruit first but be suspicious of easy answers.
Add targets and dependencies#
To “think” (assuming you can’t read or write) would be to make a modification in your own BNN (i.e. without going external, something you could do with your eyes closed). Many great ideas come to people in their sleep. This action depends on nothing but your own mind (which includes an objective function), and targets your own mind.
If you have a large body of personal notes, what ideas have you forgotten about in them? For the advantages of exploring your own notes, see Organize notes. This action depends on your mind and notes, and targets your mind and notes.
You should be able to follow any process from your public notes (e.g. on a car ride); you regularly publish all these thoughts. Start from public so you focus on quality first, and you get to see the rendered version of articles (easier to follow links, fix easy issues). Following a public process also helps you identify what needs to be publicized most, and it helps you see the big picture (because of the TOC on the right). If you’re only reading your public notes, you’ll also only go to them when you want to add notes.
You’ll almost surely start to open these articles in vim, and you’ll see anything that is different in your personal notes from your public notes. While you are only exploring, limit yourself to fixing minor errors, annotating (i.e. only adding comments), and removing comments. If you want to restructure (e.g. move and rename files), create a new TODo, finish your current task early, and return to planning (compare the task to at least one other, then commit to the new work). This approach also works well with git; it will be obvious in your history when you e.g. renamed files and moved content between articles because your changes will be more limited to a rename or move.
You can see this cycle as producing commits in a branch that are limited to one of two types of commits. The first is what is typically described as formatting and commenting (non-functional) commits, analogous to planning commits. The second is refactoring or feature commits, analogous to finishing TODo nodes in the git graph (or giving up on them). You can produce this kind of history from the beginning, or rebase your branch several times to make it look “clean” i.e. easier to review by others.
In a net we literally use random number generation to create ideas; this step is assisted by the loss function through the “educated guess” system of backpropagation. Other aspects of the guessing system are the loss function, AdaGrad, etc. A human instead sets goals, also using their “loss function” (system of values) as guidance (though they also may discover after more thought that a goal has negative value). To “generate an idea” is the same as setting a goal; these are essentially equivalent assuming that the idea adds value (or you believe without further thought that it does).
Said another way, the “whole world” to the training algorithm is all the weights in the model it is training. Backpropagation considers the “whole world” to propose an update. Humans more often focus on only part of the world at once, making an assumption that different parts of it can be worked on independently.
Consider the actual physical exploration of an area. This takes as input (depends on) the state of the area, and your own mind, and targets an update to your mental map of the area. If you go to the area with certain values in mind (e.g. to go higher) then as you explore the area you’ll also be targeting ideas (TODO-x). Exploring a mountain is different than the prairie; the domain matters.
Reviewing research (e.g. from OpenAI) is exploration. Browsing the internet (web searching) is exploration. These actions depend on the current existence of websites (ignoring the potential for future broken links, if you want to repeat the action) and targets updates to your own mind and potentially your notes. You mind has pointers to articles it has read in the past, a kind of dependency.
The huge advantage of reviewing external research is you don’t have to reinvent the wheel. External sources will advertise not only the value in e.g. a concept, but provide reasonable examples (perhaps even pedagogical material) to go off of. The abstract in a paper usually includes the equivalent of an “Estimate value” function, and the length of the paper (or the parts you care about) provide an “Estimate cost” function. Web articles always start with the author trying to sell you on how important their content is before they get into the details (see JustTheRecipe).
In terms of a net, what if someone has already done backpropagation, made an educated guess, and has a weight update you can use? Or a whole pretrained model? As long as you can reproduce their results as needed, you should be able to rely on their work as a dependency (ignoring their failed experiments, unless they were kind enough to document them).
Imagine you had an oracle would could indicate to you where to search for value, and in fact how to achieve any goal you were interested in. That is, you had no need to backpropagate (or plan) because an oracle could give the plan to you. What would this look like for implementing code? It’d have to be based on what the developers you have already know; the most efficient steps would likely use what they know (low cost steps) to do work while expanding their knowledge incrementally (adding abstractions). In some cases, though, it might ask them to e.g. read something in detail to obtain new abstractions to be able to implement the next feature extremely quickly. The oracle would need to know how long every developer would stay at the company, to decide how much to invest in them.
The next best thing to an oracle is a teacher who understands both what you do and don’t know, what options are out there for you to explore next, and what you value. Equivalently, you could follow an easy to use map of the territory produced by someone else (a map where X marks the spot with the gold).
Again, the answer is domain-specific. Such a map exists in some domains (e.g. in existing research papers). In these domains, you should be reviewing the research rather than exploring your own notes and ideas. If you don’t know the word that describes what you want to do you can’t ask the question, however, even if the research already exists. More words always makes for better and more specific questions, even when you’re querying yourself. We can only come to a teacher or map (e.g. a web search engine) with our own words. See also:
A kid who can’t express what they want must resort to crying (and their parents guessing). Even a good parent who understands their kid’s babble and sign language can’t run the neurons to help the kid hit the next milestone, though. It may take some time to organize your own thoughts to be able to ask the right question, or the teacher/map may simply be confused and provide irrelevant results (web search engines come with safety filters). In fact, you aren’t going to know if the existing research has a solution to your questions until you’ve been able to query it, and that may require learning new language specific to the research community in order to do so.
Use domain-specific heuristics#
Adding goals/ideas is like mining. You can’t be sure that a particular mine will produce the same amount of gold per unit of work it did in the past, but you can take as a prior the gold per unit work it produced in the past.
That is, you should have some heuristic (per domain) for the number of imagined worlds you should be considering before you start working on one of them. It’s only with experience in an area that you’ll be able to choose a good default before starting, but after collecting even a few goals/ideas you’ll get a sense of it. Is your new idea twice as valuable as the last one you generated? It’s probably always wise to generate 3-4 ideas in what you consider a “domain” before picking out one of them.
This is another fundamental limitation of planning; who says there isn’t gold around the next corner? How many ideas should you be considering before you move on one of them? This isn’t about your values shifting, but simply about you being unaware of what opportunities are out there. It’s only with experience that you’ll be able to come up with a number (a hyperparameter) for a new domain (e.g. the minimum, average, or maximum number of ideas you should generate).
Try to avoid succumbing to your personality, and instead consider the domain. One of the Big Five personality traits is curiosity, for example. Is curiosity good or bad? It depends on the domain. See the article Reinforcement Learning with Prediction-Based Rewards, which points out the value in curiosity in solving Montezuma’s Revenge. The addition of curiosity is probably beneficial overall (some is almost always better than none), but on certain games the addition of curiosity led to lower performance.
When exploration/experimentation/investigation is dangerous or expensive, we use the proverb “curiosity killed the cat” to control ourselves. People crave new experiences; we don’t want to go to the same restaurant twice and will spend half an hour searching for a new place to eat (despite the limited value). If you can’t predict it, we like it.
Still, there’s likely value in tuning towards curiosity in modern problems. The curiosity mechanism that OpenAI used in the reference above is quite naive; it puts intrinsic value on novelty when most people would say learning only has instrumental value. Said another way, the curiosity mechanism doesn’t involve any kind of introspection on the part of the model to check whether it’s building any kind of causal (reusable) abstraction/subnet, much less a highly valuable abstraction.