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This makes me wonder if games that "require" the player to read the manual first can be played by a similar agent with/without reading the manual. Or perhaps e

by arayh 8y ago

This makes me wonder if games that "require" the player to read the manual first can be played by a similar agent with/without reading the manual. Or perhaps even more difficult are the games that even humans struggle to play through to the end due to "cryptic gameplay elements" (of which there are a lot of examples in Atari games).

ASalazarMX 8y ago
I seriously doubt this AI can play games like Adventure, Raiders of the Lost Ark or the Swordquest series, where there is no score tally and a complex sequence of actions is needed to win the game.
- jhabdas 8y ago
  They can't. You're still superior and always will be until human extinction or slavery. Speaking of slavery, how did you get here?
  - dang 8y ago
    If you continue to post uncivil and unsubstantive comments to HN, we are going to ban you. We've asked you several times already. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
- tialaramex 8y ago
  Like a human player the AI needs to have some idea whether it's succeeding. Actually, an AI needs this slightly less than humans because it's so purposeful anyway - it's not as though it can get bored and go watch TV. But it needn't be displayed as a "score" number, there's no score in Chess for example, and that didn't stop Deep Mind's chess engine. In Advent for example there are clear sub-goals and an AI can be programmed to try to achieve these, you actually get points in Advent (all versions I think?) for partial completion, so it's just a matter of exposing that to the evaluation function, but other evaluation functions might work as well, or even better.
  - ASalazarMX 8y ago
    Isn't this the general AI that plays Atari games by watching? It would have to guess the rules of Adventure. If it doesn't evolve into a corner, it probably could master those games given enough time, but how much is enough time?
    - MrEldritch 8y ago
      The standard "hard game" of that category is Montezuma's Revenge - which has no score, is easy to die right on the first screen, and requires a fairly complex series of actions to make any clearly visible progress (moving to the second screen) at all. Deep Q Networks (the Atari AI you're thinking of) and really all reinforcement learning systems generally fail to make it past even the first screen.
      - tialaramex 8y ago
        The POPART paper actually shows that Montezuma's Revenge is one of the games in the standard set of Atari games they tested, and yup, their AI was no good at it.
        dane-pgp 8y ago
        That game (and others like it) would probably be much easier if instead of optimising for score they optimised for "game states (pixel arrangements) that the AI hasn't seen before". This metric should correlate well with moving the player character around each screen, and getting to new screens, as well as picking up objects. I believe some researchers have tried this approach and had some success, but I'd like to see how far DeepMind (for example) could take it.

by arayh 8y ago

ASalazarMX 8y ago

I seriously doubt this AI can play games like Adventure, Raiders of the Lost Ark or the Swordquest series, where there is no score tally and a complex sequence of actions is needed to win the game.

jhabdas 8y ago
They can't. You're still superior and always will be until human extinction or slavery. Speaking of slavery, how did you get here?
- dang 8y ago
  If you continue to post uncivil and unsubstantive comments to HN, we are going to ban you. We've asked you several times already. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
tialaramex 8y ago
Like a human player the AI needs to have some idea whether it's succeeding. Actually, an AI needs this slightly less than humans because it's so purposeful anyway - it's not as though it can get bored and go watch TV. But it needn't be displayed as a "score" number, there's no score in Chess for example, and that didn't stop Deep Mind's chess engine. In Advent for example there are clear sub-goals and an AI can be programmed to try to achieve these, you actually get points in Advent (all versions I think?) for partial completion, so it's just a matter of exposing that to the evaluation function, but other evaluation functions might work as well, or even better.
- ASalazarMX 8y ago
  Isn't this the general AI that plays Atari games by watching? It would have to guess the rules of Adventure. If it doesn't evolve into a corner, it probably could master those games given enough time, but how much is enough time?
  - MrEldritch 8y ago
    The standard "hard game" of that category is Montezuma's Revenge - which has no score, is easy to die right on the first screen, and requires a fairly complex series of actions to make any clearly visible progress (moving to the second screen) at all. Deep Q Networks (the Atari AI you're thinking of) and really all reinforcement learning systems generally fail to make it past even the first screen.
    - tialaramex 8y ago
      The POPART paper actually shows that Montezuma's Revenge is one of the games in the standard set of Atari games they tested, and yup, their AI was no good at it.
      - dane-pgp 8y ago
        That game (and others like it) would probably be much easier if instead of optimising for score they optimised for "game states (pixel arrangements) that the AI hasn't seen before". This metric should correlate well with moving the player character around each screen, and getting to new screens, as well as picking up objects. I believe some researchers have tried this approach and had some success, but I'd like to see how far DeepMind (for example) could take it.