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A heuristic search using MCTS + minimax + alphabeta pruning is a highly efficient AI planning process. What the AI techniques of reinforcement learning (RL) plus neural networks (NNs) typically add to this is a way to establish better heuristics. My intuition tells me that this is way harder and far more complex. It's not actually that much more complex ...


3

Would it be helpful to use a LSTM and reduce the input state? I'd bet, no. LSTM is more complicated and harder to learn, while the input is 4 * 9 * 36 bits is still rather limited. However, you may want to aggregate the information somehow, e.g., add additional bits informing about what cards were already played (no matter when). This information is ...


1

There is indeed a close parallel here, but the concepts are distinct. Every perfect information game is fully observable, but not every fully observable game is a game of perfect information. A game of imperfect information is one in which you lack knowledge of any of the following: The state of the game (e.g. current market prices). The rewards you will ...


1

Not exactly, at least traditionally: in Game Theory, "imperfect information" is most often defined as agents having only partial information about the history of agents' actions, as you correctly noted. But also note that this doesn't refer to the general world facts or state. But "partial observability" is typically used in terms of systems, e.g. in Markov ...


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