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John's answer is correct in that MCTS is traditionally not viewed as a Machine Learning approach, but as a tree search algorithm, and that AlphaZero combines this with Machine Learning techniques (Dee …

So far, I have considered only three algorithms, namely, minimax, alpha-beta pruning, and Monte Carlo tree search (MCTS). Apparently, both the alpha-beta pruning and MCTS are extensions of the basi …

You can use a set (in the mathematical sense of the word, i.e. a collection that cannot contain duplicates) to store states that you have already seen. The operations you'll need to be able to perform …

Genetic algorithms and Neural Networks both are "general" methods, in the sense that they are not "domain-specific", they do not rely specifically on any domain knowledge of the game of Mario. So yes, …

Typically, Monte-Carlo Tree Search (MCTS) actually is the go-to "solution" for such problems with large branching factors. I can understand that "vanilla" MCTS may still have unsatisfactory performanc …

The primary issue I see is that in the loop through time steps t in every training episode, you select actions for both players (who should have opposing goals to each other), but update a single q_ta …

First, we need to distinguish plain Monte-Carlo from Monte-Carlo Tree Search. They're different things. Monte-Carlo search, in the context of game AI search algorithms, is typically understood to me …

There has indeed been some research towards combining MCTS and minimax-like algorithms. For example, the following two publications: Monte-Carlo Tree Search and minimax hybrids Monte-Carlo Tree Sear …

The most "standard" implementation of MCTS probably involves storing copies of game states inside nodes. This works fine for deterministic games, but not for non-deterministic games due to the reasons …

I don't think your network, trained using PPO to play a card game, already contains sufficient information to also use for drafting. I'm not saying this with 100% certainty, maybe there's something I' …

I don't think that's necessarily a strange number. It's impossible for anyone to really tell you whether that 17% is "correct" or not without reproducing it, which would require much more info (basica …

First thing you're going to want to add is probably a Transposition Table, as also suggested by SmallChess. Afterwards, I'd look into Aspiration Search and/or Principal Variation Search (also see thi …

When one agent makes a move, that move should be perceived as part of the "state transition" executed "by the environment" from the perspective of the other agent. For example, suppose that, as a "ne …

The second equation is correct. In TD($\lambda$), the $\lambda$ parameter can be tuned to smoothly vary between single-step updates (essentially what Sarsa does) in the case of $\lambda = 0$, and Mont …

Intuitively I kind of doubt expecting a search depth of 10 in half a second is reasonable, especially for the initial game state where there's a rather large branching factor and no immediately-winnin …