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I think this technique -- in the title of the paper -- would transfer somewhat to other games, because it essentially encourages the player to go places it hasn

by wrsh07 10y ago

I think this technique -- in the title of the paper -- would transfer somewhat to other games, because it essentially encourages the player to go places it hasn't been [without simultaneously having it explore everything thoroughly]. So you use existing tools to encourage the player to survive, and then you add additional motivation to encourage the player to learn what it needs to do [ie explore]. And exploration is a common theme in games like Zelda. I actually think that this technique will generalize to learning lots of games [eg the same engine might play Zelda & Montezuma's Revenge], but I don't think techniques in this vein alone will allow it to learn how to play one game and then immediately understand how to play the other. [but "transfer learning" is a separate, challenging problem in AI]

chongli 10y ago
That's a shame. For a human, each game played helps to make the person better at all games. Play enough adventure games, for example, and one begins to recognize so many of the patterns and tropes at work that the solutions to puzzles jump out obviously and immediately in a single play-through. Moreover, we already have machines that are extremely proficient at solving very complex games when given enough context: expert systems. One very notable example is the bot which successfully completed the game NetHack [0]. Would DeepMind's novelty-based reward technique work for NetHack? [0] https://github.com/krajj7/BotHack https://github.com/krajj7/BotHack
- wrsh07 10y ago
  Agreed. I think it's a pretty big open problem for ML / AI. How can learning about one thing help you do better at a related thing? Because you're absolutely right that a human who is good at one platformer will take very little time to adjust to a new one. I don't know much about NetHack, but seeing that it's a Roguelike, I think these new techniques should definitely be tried on it or something similar.

by wrsh07 10y ago

chongli 10y ago

That's a shame. For a human, each game played helps to make the person better at all games. Play enough adventure games, for example, and one begins to recognize so many of the patterns and tropes at work that the solutions to puzzles jump out obviously and immediately in a single play-through. Moreover, we already have machines that are extremely proficient at solving very complex games when given enough context: expert systems. One very notable example is the bot which successfully completed the game NetHack [0]. Would DeepMind's novelty-based reward technique work for NetHack? [0] https://github.com/krajj7/BotHack https://github.com/krajj7/BotHack

wrsh07 10y ago
Agreed. I think it's a pretty big open problem for ML / AI. How can learning about one thing help you do better at a related thing? Because you're absolutely right that a human who is good at one platformer will take very little time to adjust to a new one. I don't know much about NetHack, but seeing that it's a Roguelike, I think these new techniques should definitely be tried on it or something similar.