In these two papers, the policy parametrization is roughly the same, except that ES uses virtual batchnorm. The training and parameter storage is not done with preemptibles.Also one could look at the cost of the custom development of bots and AIs using other more specialized techniques: sure, it might require more processing power to train this network, but it will not require as much specialized human interaction to adapt this network to a different task. Or alternatively if you frame it “king” – “man” + “woman”, then “king” – “man” produces vector of a “ruler” and that added to “woman” produces “queen”. So this makes it a little bit less of a local optimal. Otherwise it's still "cheating" in some sense vs how a human would be forced to play.>OpenAI Five is given access to the same information as humans, but instantly sees data like positions, healths, and item inventories that humans have to check manually. Our results suggest that we haven’t been giving today’s algorithms enough credit — at least when they’re run at sufficient scale and with a reasonable way of exploring.which is mostly about the challenge of longer time horizons (and therefore LSTM related). Also, you can only issue orders on what is directly visible to you, so if you pan away from your character that restricts what you can do.Is OpenAI Five modeling this aspect of the game? on voice, it takes a few seconds to give orders. It makes a very generic way to represent different units to the network. Agreed that a follow-up detailed post or paper would be awesome!Far too many hyperlinks though.
And we do know that bots are going to be good at early game last hitting.The article states the bots are actually rather mediocre at last hitting.I've played DotA for over 10 years so this development is quite relevant to me. The main goal will be to kill innocent folks with this type of AI research. The policy space remains large, and the human is not doing a dumb search, because the human does not have billions of games to work with.> See our gameplay reel for an example of some of the combos that our system learns> Our system learns to generalize behaviors in a sophisticated way.You're underestimating random search. That could add up quick... e.g.
These folks are working for CIA without noticing the involvement of The Spy Agency. It's more clear that the progress in image processing was important because it resulted in self-driving cars.Firstly, research into Chess AI has had a surprising amount of beneficial spin-off, even if we don't call the result "AI".Another benefit of showing off progress with games is it allows the everyday reader to follow and understand it as well. It's cool, does seem a little bit greedy, that is still expensive as buck yo. I.e. So perhaps it would be more fair to say that gradient descent (not random search) has proven to be a pretty solid foundation for model-free reinforcement learning.Yes, I am aware, I did not mean random search as in random actions, but random search with improved heuristics to find a policy.When optimizing high-dimensional policies, the gap in sample complexity between PPO (and policy gradient methods in general) and ES / random search is pretty big. But this probably slows the learning.Some people suggested auto-encoder to compress the world, and then feed it to a regular CNN.1) "At the beginning of each training game, we randomly "assign" each hero to some subset of lanes and penalize it for straying from those lanes until a randomly-chosen time in the game...." Combining this with "team spirit" (weighted combined reward - networth, k/d/a). Q-PROP), namely the specific clipped objective, subsampling, and a (in my experience) very difficult to tune baseline using the same objective, do not significantly improve over gradient descent.And I think Ben Recht's arguments [0] expands on that a bit in terms of what we are actually doing with policy gradient (not using a likelihood ratio model like in PPO) but still conceptually similar enough for the argument to hold.So I think it comes down to two questions: How much do 'modern' policy gradient models improve on REINFORCE, and how much better is REINFORCE really than random search? Max-pooled results for each of the unit types are concatenated and then fed into the LSTM. OpenAI Five bots use Dota 2 API to “see” surrounding units and buildings. A lot of Dota expertise derives from knowing what the opponent is going to do at a particular time. I don't play DoTA, so not sure how many such heroes exist, or if a team comp of only heroes with targeted abilities would even be viableBot Crystal Maiden was killing allright by casting non-targeted spells into the fog (where the enemy ran to hide).It sounds like 170,000 is every possible combination of actions that might ever be valid. Embedding is a natural way to represent things with many different but potentially overlapping qualities, and which might have a similar effect but to different extent. RL (and ML generally) definitely works better as you add more scale, but I still feel that this particular work is roughly "grand challenge" level. Who clicks on hyperlinks for words like "defeat", "complex", "train" and "move"?
60grand a day to use hundreds of thousands of cores to learn to play computer games, roughly 1.8mill for 30 days of active learning. But, again, it seems to work fine. E.g. Access to last hits @ 10 mins, gold and net worth graphs would allow us to answer that question.How does training RL with preemptible VMs work when they can shut down at any time with no warning? Again this seems to be questionable at the first sight, because different qualities of nearby units would be combined, e.g. The 256 "optimizers" cost less than $400/hr, while if you were using regular cores the 128k workers would be over $6k/hr. This poses an enormous exploration problem for RL, because initially the agent starts by just trying out random actions.
It has been shown that when a network is trained to predict from word vector the vectors of its surrounding words, the word vectors acquire semantic meaning and you can do arithmetic operations with them.
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