Flash on DeepMind: "I think I can win" - Page 8

On March 11 2016 13:01 ZAiNs wrote:
Deep learning needs a dataset for the AI to be trained though. For AlphaGo they trained two separate networks (one designed to predict the next move, and the other designed to predict the final winner) on 30 million discrete moves from games played by human experts. After that it trained itself by actually playing Go against itself a ridiculous number of times.

A Go game can be perfectly modelled by simple list of positions describing which square had a stone placed on it each turn, it's going to be very hard to get enough useful data (replays) to significantly help with the training. And without the initial training it's going to have to learn mostly by playing against itself which will be difficult because of the ridiculous number of game states. At least that's my understanding of things, I could be wrong, but it seems to be a lot harder than Go.

On March 11 2016 22:47 MyLovelyLurker wrote:
I've been watching Broodwar for 15+ years, and programming reinforcement learning engines for a few. Here are a few thoughts on why the specific DeepMind approach is going to be very hard for SC, although it might well happen in around 10 years time :

1. We are assuming the AI teaches itself to play only from a realtime view of the pixels on the screen, and knows nothing about any score at all - *there is no score in Starcraft*, unlike in the arcade Atari 2600 games that have been mostly arcade games, with a clear numerical objective ( the score ) to be maximized by the playing agent. The act of playing thereby becomes a calculus problem ( to first order, set the gradients of the score function to zero ). Not impossible but harder in Starcraft.

2. Starcraft II is an imperfect information game, as opposed to chess or go where the board contains the whole information available to both players. Whilst it is possible to do reinforcement learning in that setting, it is a relatively new field and adds to the difficulty - articles are being published now on the subject.

3. The 60 or 120 APM barrier will not be broken easily. Right now in the Atari 2600 Deepmind simulations rely on one or two actions by frame, which imposes that your APM is limited by the FPS you render. Even with two policy networks - one for the keyboard and one for the mouse - you are headbutting against 120APM pretty much. It is not impossible to think about operating several policy networks in parallel in order to enable strong ( think Korean multiple drops ) multitasking, but it is a new area that needs to be explored - the connections between networks and their interaction would need to be thought through carefully. Some cutting-edge research with asynchronous networks goes in a similar direction.

4. Point-and-click games have not been tackled yet by RL ; they are joystick or keyboard-based, ergo with binary 'push or don't push' states, but no mouse game has been tackled by a policy network as far as I know. This brings its own sets of challenges ( the AI will have to figure out by itself, for instance, how to move the mouse in optimal ways, which includes making straight lines, position the cursor close to a Nexus or a pylon, etc ).

5. Starcraft is also 'multi-screen' - it requires frequently changing views with your F keys ( move to different bases and engagement battles ) in order to correctly represent the full state of the game. So far, Atari 2600 games have been mono-screen only. Again, it is not impossible to imagine this will be overcome in the future, it is just harder to do right now.

6. Combinatorial explosion in the number of unit compositions is also hard to tackle - every time you add a potential unit to the mix, the possibilities for army composition multiply, which is why in the campaign mode you learn to play from introduces units pretty much one at a time. It would objectively be much, much harder to start playing full games from laddering and without an instruction manual, which what the Deepmind approach is.

7. The meta in SC rotates on a regular basis - it is 'non stationary', which adds to the list of problems encountered by a machine that would learn by playing on ladder, as some of the strats and playstyles learned earlier could well be obsolete - and hard-countered - by the time they are assimilated. This happens with human players too ; they have to make a conscious effort to get out of a slump, learn more new information, and forget about the old. Some work on policy distillation or optimal brain damage in neural networks goes, very tentatively, in that direction. Again, this is hard.

For all those reasons, it would be an incredible achievement already to have a Starcraft deep reinforcement learning AI that can teach itself to play a very easy computer AI in a setting with only workers, and maybe a unit list restricted to just a couple, like zealots and dragoons.

If you look at the performance of reinforcement learning in 2d games such as Atari, 'mechanical' games like Pong or Breakout get to much higher skill levels than games with planning required such as Pacman. It is hence entirely possible that Starcraft Deepmind would play mechanically correctly, but overall pretty poorly, as one can only speculate. If you add up all the objection points above, you can get a feel for why there is quite a long way to go.

Happy to provide reference articles list if required.

[B]1. We are assuming the AI teaches itself to play only from a realtime view of the pixels on the screen, and knows nothing about any score at all - *there is no score in Starcraft*, unlike in the arcade Atari 2600 games that have been mostly arcade games, with a clear numerical objective ( the score ) to be maximized by the playing agent. The act of playing thereby becomes a calculus problem ( to first order, set the gradients of the score function to zero ). Not impossible but harder in Starcraft.

On March 12 2016 22:08 Superbanana wrote:
Hard? 10 years? Are you kidding?
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