Probability Tree State Abstraction

Probability Tree State Abstraction (PTSA) aims to improve the efficiency of Monte Carlo Tree Search (MCTS), a core algorithm behind many successful AI systems, by reducing the complexity of its search space. Current research focuses on developing PTSA methods that minimize errors introduced during state aggregation within the MCTS tree, often integrating them with advanced MCTS variants like MuZero. These advancements lead to faster training and potentially improved performance in various applications, offering a significant contribution to the scalability and practical applicability of MCTS-based algorithms.