Body: <p>As far as I can tell (correct me if I'm wrong), Alphazero (with MCTS and neural network heuristic function RL) is the state of the art training method for turn based, deterministic, <em>perfect</em> information, complete information, two player, zero sum games.</p>
  
  <p>But what is the state of the art for turn based, <em>imperfect</em> information games, that have 2 players, complete information, and is zero sum? (Deterministic or stochastic.) Examples include Battleship and most 2 player card games.</p>
  
  <p>Are there standard games, or other tests by which this is measured? Is the criteria I offered for type of game not specific enough to narrow the answer down properly?</p>
  
  <p>If the state of the art involves supervised learning (data set of manually played games), then what's the state of the art for pure reinforcement learning, if there is one?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-10-03T20:12:22.340Z
Comments.Text: Games with more than 2 players are often a very different setting on just that basis (for instance many strategies are not stable if the game rules allow two players to conspire against another one), so maybe worth focusing on either that part, or the imperfect information part
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-10-03T20:14:53.287Z
Comments.Text: @NeilSlater If I don't include multiple players and just focus on imperfect information, wouldn't there be a different answer for the single player case vs multiple players?
Comments.UserDisplayName: Bridgeburners
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-10-03T20:20:50.027Z
Comments.Text: Yes there would potentially be a different answer. If you are looking for both multiple (3 or more) players and imperfect information (player A can know something that player B does not), then please specify that clearly, perhaps with better examples. For instance, Battleship is usually a two-player game - are you considering some 3+ player variant of it?
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-10-03T20:25:03.830Z
Comments.Text: @NeilSlater Ok, I was hoping the answer wouldn't be too different for any number of players > 1, but if it is, then I prefer the answer for 2 player games. I'll edit my question to specify that.
Comments.UserDisplayName: Bridgeburners
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-10-03T20:29:52.077Z
Comments.Text: Some 3+ player games that are designed like competitive races, will behave very similarly to 2 player games. Others, such as where players can choose to block or attack another players' position, open themselves up to strategies based around group allegiances, and these can become complicated.
Comments.UserDisplayName: Neil Slater
Tags: <ai-design><training><game-theory><markov-decision-process><imperfect-information>
Title: What is the state of the art AI training technique for imperfect information 2 player turn based games?
UserDisplayName: Bridgeburners
ThreadText: # What is the state of the art AI training technique for imperfect information 2 player turn based games?
  
  (Asked by: username_0 on 2019-10-03)
  
  As far as I can tell (correct me if I'm wrong), Alphazero (with MCTS and neural network heuristic function RL) is the state of the art training method for turn based, deterministic, *perfect* information, complete information, two player, zero sum games.
  
  But what is the state of the art for turn based, *imperfect* information games, that have 2 players, complete information, and is zero sum? (Deterministic or stochastic.) Examples include Battleship and most 2 player card games.
  
  Are there standard games, or other tests by which this is measured? Is the criteria I offered for type of game not specific enough to narrow the answer down properly?
  
  If the state of the art involves supervised learning (data set of manually played games), then what's the state of the art for pure reinforcement learning, if there is one?
  
  **Tags:** ai-design, training, game-theory, markov-decision-process, imperfect-information
  
  > **Comments:**
  > **username_1**: Games with more than 2 players are often a very different setting on just that basis (for instance many strategies are not stable if the game rules allow two players to conspire against another one), so maybe worth focusing on either that part, or the imperfect information part  
  > **username_0**: @username_1 If I don't include multiple players and just focus on imperfect information, wouldn't there be a different answer for the single player case vs multiple players?  
  > **username_1**: Yes there would potentially be a different answer. If you are looking for both multiple (3 or more) players and imperfect information (player A can know something that player B does not), then please specify that clearly, perhaps with better examples. For instance, Battleship is usually a two-player game - are you considering some 3+ player variant of it?  
  > **username_0**: @username_1 Ok, I was hoping the answer wouldn't be too different for any number of players > 1, but if it is, then I prefer the answer for 2 player games. I'll edit my question to specify that.  
  > **username_1**: Some 3+ player games that are designed like competitive races, will behave very similarly to 2 player games. Others, such as where players can choose to block or attack another players' position, open themselves up to strategies based around group allegiances, and these can become complicated.  
  
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  ## 0 answers
  
  