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Depends on perspective. On one hand, you have an agent playing in an environment with another agent also evolving. This falls under the definition of Multi-Agent Learning, as can be seen with works such as Michael Bowling and Manuela Veloso. Multiagent learning using a variable learning rate. Artificial Intelligence, 136(2):215 – 250, 2002. Michael Bowling....


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it was "just given the rulebook", what does this mean? Literally a book written in English to read? The program was not given a natural language version of the rules to interpret. That might be an interesting AI challenge in its own way, but none of the current cutting-edge game playing reinforcement learning systems do much in the way of natural ...


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"Will a neural network adapt to that ?" No. The big functional difference between human mind and neural networks : human mind learns by itself, a NN not. If we call NN the net with its layers, weights, ... this is a static system, unable to learn anything new. The back-propagation algorithm that made intelligent the NN runs outside the NN itself, ...


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The behaviour when playing against "cheats" depends on how the agent has been trained, and how different the game becomes from the training scenarios. It will also depend on how much of the agent's behaviour is driven by training, and how much by just-in-time planning. In general, unless game playing bots are written specifically to detect or cope ...


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The most important word for answering your question from that quote from the paper is probably the word "usually": These search probabilities usually select much stronger moves than the raw move probabilities $p$ of the neural network. It's not always going to be true, but more often than not / most of the time / "on average", we ...


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However, I’m not sure which policy is saved The policy from the Monte Carlo tree search is stored, as we can get the policy estimate from the network later by passing the given state through the network, which is used to calculate the cross entropy loss to update the network's policy (summed with Mean squared error loss between value head's prediction and ...


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The evaluation of the last steps in the game can be made with the 1 and 0 as you said. For all the other steps, the evaluation should be the evaluation of the best next step with a small decay.


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Welcome to the mine-field of semantic definitions within AI! According to Encyclopedia Britannica ML is a “discipline concerned with the implementation of computer software that can learn autonomously.” There are a bunch of other definitions for ML but generally they are all this vague, saying something about “learning”, “experience”, “autonomous”, etc. in ...


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