德州扑克策略：别以为低额现金桌容易赢，不知道这些你照样输
德州扑克策略：别以为低额现金桌容易赢，不知道这些你照样输
战斗到黎明
我们的德州扑克游戏现在招募游戏测试用户，可免费领取游戏币，赢得比赛有奖品！关注微信公众号：德扑精华区，回复“测试+本人微信号”，即报名成功！下面进入正题。跟其他游戏类型比较起来，现金桌有许多优势。您可以选择下注单位与参赛金，换句话说各种等级的玩家都有适合的现金桌。您随时可以上桌与离桌，想要玩几分钟或是几小时都可以。这点与多桌锦标赛相当不同，因为参加多桌锦标赛时，您必须玩到被淘汰或是获胜为止。最棒的是，低額现金桌其实并不困难。以下策略能帮助您在低额现金桌成为常胜军。虽然会有少数例外，但是通常低额现金桌玩家都会犯下代价昂贵的错误。我们的工作就是要尝试利用这些错误从中获利。最常见的错误就是他们玩太多牌局，而且不论是在翻牌圈前后都不喜欢盖牌。如何充分利用这种错误？很简单，减少诈唬次数并且提高加值下注次数。常常您可能有办法高明地诈唬对手，然后开始对他们讲垃圾话，结果他们跟注并且用很弱的底牌赢得牌局。但如果您想要诈唬不容易上当的对手，那么犯下代价昂贵错误的玩家就是您。所以底池小时，就算您很想要诈唬也请直接盖牌，让对手赢得牌局。但是如果您组成牌组，即使只是中等强度，也请务必每一轮都下注。通常此时跟注的对手，牌组都比您的弱，而您从这些底池所得到的价值，足以弥补先前放弃以及过牌所输掉的底池。在这些牌局中，您也有可能对上太常诈唬的玩家！这些玩家在牌桌上都相当危险，不过他们必须选对诈唬对象才能得逞。但是低額牌局中，这种情况很少发生。通常大多数的诈唬都没什么道理可言，所以很容易发现。例如，翻牌结果有机会组成同花（例如梅花Q、梅花8与黑桃2），您的对手却过牌、跟注。转牌是无助于组成同花的方块3，但对手依旧过牌跟注。河牌则是红心2，同样无助于组成同花，但此时对手带头下注且赌注高达底池100%。此时只要您有组成任何牌组，就应该跟注。当然您的对手可能是在诱敌，或是在河牌时好运组成三条，不过最有可能的情况是他们组牌失败，所以想要诈唬赢得底池。若某玩家的行动不合常理，通常就表示他在诈唬。最后一点，您应该观察对手的下注模式，可从中获得许多信息。好玩家会努力维持一致的下注金额大小，不但可以避免对手摸清他们的策略，同时获得最大的价值。牌技差的玩家，就像您在低額现金桌会遇到的玩家，通常无法维持一致的下注金额规模。牌技差的玩家在决定下注金额时最常犯的错误如下：持有弱牌却在翻牌前小额加注，底牌很强时却大额加注。组成最佳牌组时在翻牌后下注且赌注达底池的100%。只有组成最佳牌组时才超额下注翻牌后组成中等强度的牌组却只下最低加注，代表他们希望用最少的筹码进入摊牌阶段牌组弱却在翻牌后下注1个大盲注（此时您务必要加注！）希望这些诀窍能够帮助您，在低額现金桌享有优势。请务必切记维持纪律，聚焦基本动作，并注意对手的行动。如果您可以做到，相信您很快就能更上一层楼。
本文仅代表作者观点，不代表百度立场。系作者授权百家号发表，未经许可不得转载。
战斗到黎明
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【精品】德州扑克基本常识(规则、术语、基本策略、各种技巧)
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官方公共微信为什么德州扑克容易使人入迷？_百度知道
色情、暴力
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为什么德州扑克容易使人入迷？
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DUBO都容易使人入迷，德州扑克更是变幻无穷，魅力无限
虚拟赌博游戏嘛，输了就想赢回来，赢了就想再赢多点
千变万化，引人入上
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等待您来回答求推荐一种既需要一定的智商又简单易操作的赌博游戏？ - 知乎8被浏览1276分享邀请回答0添加评论分享收藏感谢收起1添加评论分享收藏感谢收起查看更多回答1 个回答被折叠（）怎么看AI首次在德州扑克战胜人类职业玩家？ - 知乎72被浏览7227分享邀请回答arxiv.org/pdf/4v1.pdfDeepStack becomes the first computer program to beat professional poker players in heads-up no-limit Texas hold’em.43 条评论分享收藏感谢收起skyocean117.blogspot.co.nz/2013/12/introduction-to-cmac-neural-network.html下面是扑克人工智能Libratus的设计理论，项目主任的讲座视频，大家翻墙自己看去吧!However, how the opponent’s actions reveal that information depends upon their knowledge of our private information and how our actions reveal it. This kind of recursive reasoning is why one cannot easily reason about game situations in isolation, which is at the heart of local search methods for perfect information games. Competitive AI approaches in imperfect information games typically reason about the entire game and produce a complete strategy prior to play (14, 15).2 Counterfactual regret minimization (CFR) (11, 14, 17) is one such technique that uses self-play to do recursive reasoning through adapting its strategy against itself over successive iterations. If the game is too large to be solved directly, the common solution is to solve a smaller, abstracted game. To play the original game, one translates situations and actions from the original game in to the abstract game.While this approach makes it feasible for programs to reason in a game like HUNL, it does so by squeezing HUNL’s 10160 situations into the order of 1014 abstract situations.DeepStack takes a fundamentally different approach. It continues to use the recursive reasoning of CFR to handle information asymmetry. However, it does not compute and store a complete strategy prior to play and so has no need for explicit abstraction. Instead it considers each particular situation as it arises during play, but not in isolation. It avoids reasoning about the entire remainder of the game by substituting the computation beyond a certain depth with a fast approximate estimate. This estimate can be thought of as DeepStack’s intuition: a gut feeling of the value of holding any possible private cards in any possible poker situation. Finally, DeepStack’s intuition, much like human intuition, needs to be trained. We train it with deep learning using examples generated from random poker situations. We show that DeepStack is theoretically sound, produces substantially less exploitable strategies than abstraction-based techniques, and is the first program to beat professional poker players at HUNL with a remarkable average win rate of over 450 mbb/g.Continuous Re-SolvingSuppose we have a solution for the entire game, but then in some public state we forget thisstrategy. Can we reconstruct a solution for the subtree without having to solve the entire gameagain? We can, through the process of re-solving (17). We need to know both our range atthe public state and a vector of expected values achieved by the opponent under the previoussolution for each opponent hand. With these values, we can reconstruct a strategy for only theremainder of the game, which does not increase our overall exploitability. Each value in the opponent’svector is a counterfactual value, a conditional “what-if” value that gives the expectedvalue if the opponent reaches the public state with a particular hand. The CFR algorithm alsouses counterfactual values, and if we use CFR as our solver, it is easy to compute the vector ofopponent counterfactual values at any public state.Re-solving, though, begins with a solution strategy, whereas our goal is to avoid ever maintaininga strategy for the entire game. We get around this by doing continuous re-solving:reconstructing a strategy by re-solving every never using the strategy beyondour next action. To be able to re-solve at any public state, we need only keep track ofour own range and a suitable vector of opponent counterfactual values. These values must bean upper bound on the value the opponent can achieve with each hand in the current publicstate, while being no larger than the value the opponent could achieve had they deviated fromreaching the public state.5At the start of the game, our range is uniform and the opponent counterfactual values areinitialized to the value of holding each private hand at the start.6 When it is our turn to act 纳茨均衡：Exploitability The main goal of DeepStack is to approximate Nash equilibrium play, i.e., minimize exploitability. While the exact exploitability of a HUNL poker strategy is intractable to compute, the recent local best-response technique (LBR) can provide a lower bound on a strategy’s exploitability (20) given full access to its action probabilities. LBR uses the action probabilities to compute the strategy’s range at any public state. Using this range it chooses its response action from a fixed set using the assumption that no more bets will be placed for the remainder of the game.141 条评论分享收藏感谢收起查看更多回答