From the course: AI Algorithms for Gaming
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Transposition tables - Python Tutorial
From the course: AI Algorithms for Gaming
Transposition tables
- Another useful improvement for Minimax is a transposition table. It's simply a cache of previously seen states. This is sometimes known as memoization. An entry in this table contains some representation of the state and the resulting utility value. So when a node function is called, the first thing to do is to look up the state in the table. If it's found, we may confidently use the utility value from the table and return, and if it's not found, we may proceed with the usual recursion of Minimax. So, for example, this is what a transposition table would look like for chess. It's a table where each entry contains some representation of a state, and the previously found utility value for that state. It makes sense to keep highly repeating states in that table, and the lookup function must work fast, because as you may imagine, this table can get crowded very fast. This may sound like a very good optimization, and it is,…
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