4/12/2023 0 Comments Subree subramarian![]() ![]() ![]() Review my professor subree subramanian professional#Ģ012) as it allowed for a leap from 14 kyu, which is an average amateur level, to 5 dan, which is considered an advanced level but not professional yet. Before MCTS, bots for combinatorial games had been using various modifications of the minimax alpha–beta pruning algorithm (Junghanns 1998) such as MTD(f) (Plaat 2014) and hand-crafted heuristics. ![]() In contrast to them, the MCTS algorithm is at its core aheuristic, which means that no additional knowledge is required other than just rules of a game (or a problem, generally speaking). However, it is possible to take advantage of heuristics and include them in the MCTS approach to make it more efficient and improve its convergence. Moreover, in practical applications, the given problem often tends to be difficult for the base variant of the algorithm. The utilitarian definition of “too difficult” is that MCTS achieves poor outcomes in the given setting under practical computational constraints. Sometimes, increasing the effective computational budget would help, although in practical applications it may not be possible (e.g., because of strict response times, hardware costs, parallelization scaling). However, often, a linear increase is not enough to tackle difficult problems represented by trees and solving them would require effectively unlimited memory and computational power. There can be various reasons for a given problem being hard for MCTS. To name a few-combinatorial complexity, sparse rewards or other kinds of inherent difficulty. Whenever the vanilla MCTS algorithm, i.e., implemented in its base unmodified form, fails to deliver the expected performance, it needs to be equipped with some kind of enhancements. In this survey, we will focus only on such papers that introduce at least one modification to the vanilla version of the method. Although works that describe the use of standard MCTS in new domains have been published, they are not in the scope of this survey. More recently, MCTS combined with deep reinforcement learning (RL) has become the backbone of AlphaGo developed by Google DeepMind and described in the article from Silver et al. It has been widely regarded as not only another major breakthrough in Go, but in artificial intelligence (AI) in general. It is safe to say that MCTS has been tried in most of combinatorial games and even some real-time video games (Farooq et al. 2016 Kim and Kim 2017). Examples include Poker (Van den Broeck et al. 2010), Arimaa (Syed and Syed 2003), Havannah (Teytaud and Teytaud 2010) and General Game Playing (GGP) (Genesereth and Thielscher 2014 Perez et al. The latter is particularly interesting because in GGP, computer bots are pitted against each other to play a variety of games without any domain knowledge. This is where MCTS is a commonly applied approach.ĭriven by successes in games, MCTS has been increasingly often applied in domains outside the game AI such as planning, scheduling, control and combinatorial optimization. We include examples from these categories in this article as well. ![]()
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