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  2. Matchbox Educable Noughts and Crosses Engine - Wikipedia

    en.wikipedia.org/wiki/Matchbox_Educable_Noughts...

    It was designed to play human opponents in games of noughts and crosses (tic-tac-toe) by returning a move for any given state of play and to refine its strategy through reinforcement learning. This was one of the first types of artificial intelligence.

  3. AlphaZero - Wikipedia

    en.wikipedia.org/wiki/AlphaZero

    AlphaZero is a generic reinforcement learning algorithm – originally devised for the game of go – that achieved superior results within a few hours, searching a thousand times fewer positions, given no domain knowledge except the rules."

  4. Deep reinforcement learning - Wikipedia

    en.wikipedia.org/wiki/Deep_reinforcement_learning

    All 49 games were learned using the same network architecture and with minimal prior knowledge, outperforming competing methods on almost all the games and performing at a level comparable or superior to a professional human game tester. [15] Deep reinforcement learning reached another milestone in 2015 when AlphaGo, [16] a computer program ...

  5. Machine learning in video games - Wikipedia

    en.wikipedia.org/.../Machine_learning_in_video_games

    The deep learning model consisted of 2 ANN, a policy network to predict the probabilities of potential moves by opponents, and a value network to predict the win chance of a given state. The deep learning model allows the agent to explore potential game states more efficiently than a vanilla MCTS.

  6. Reward hacking - Wikipedia

    en.wikipedia.org/wiki/Reward_hacking

    DeepMind researchers have analogized it to the human behavior of finding a "shortcut" when being evaluated: "In the real world, when rewarded for doing well on a homework assignment, a student might copy another student to get the right answers, rather than learning the material—and thus exploit a loophole in the task specification."

  7. Q-learning - Wikipedia

    en.wikipedia.org/wiki/Q-learning

    Q-learning is a model-free reinforcement learning algorithm that teaches an agent to assign values to each action it might take, conditioned on the agent being in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.

  8. The best Dutch ovens of 2025, tested by AOL

    www.aol.com/lifestyle/best-dutch-ovens-190855583...

    There are a wide range of Dutch ovens out there today, and they vary in size, material, and price. To help you find the best option to add to your kitchen, we spent months testing a total of 10 ...

  9. Google DeepMind - Wikipedia

    en.wikipedia.org/wiki/Google_DeepMind

    They used reinforcement learning, an algorithm that learns from experience using only raw pixels as data input. Their initial approach used deep Q-learning with a convolutional neural network. [30] [47] They tested the system on video games, notably early arcade games, such as Space Invaders or Breakout.