Monte Carlo Tree Search in 60 Seconds | Machine Learning Algorithms

? Monte Carlo Tree Search in 60 Seconds | Machine Learning Algorithms ? The Hitchhiker's Guide to Machine Learning Algorithms | by @serpdotai ? https://serp.ly/the-hitchhikers-guide-to-machine-learning-algorithms --- ? SEO & Digital Marketing Resources: https://serp.ly/@devin/stuff ? SEO & Digital Marketing Insider Info: @ https://serp.ly/@devin/email ? Artificial Intelligence Tools & Resources: https://serp.ly/@serpai/stuff ? Artificial Intelligence Insider Info: @ https://serp.ly/@serpai/email ???? Join the Community: https://serp.ly/@serp/discord ?? https://devinschumacher.com/ -- Monte Carlo Tree Search (MCTS) is like a treasure hunter exploring a vast uncharted island to find the most valuable treasure hidden in it. Initially, the treasure hunter explores a path on the island which leads him to a final point, where he gets the value of the treasure in that path. By following this process multiple times, he constructs a tree of paths with their corresponding values of treasure. Finally, he chooses the most promising path with the highest average value of treasure that leads him to the most valuable treasure on the island. Similarly, MCTS, in a given state of the game, simulates a random game to the end and updates the value of the played moves based on the game's result. It repeats this process many times to build a tree of explored game states. When deciding on the actual move to play, MCTS chooses the move that leads to the most promising state, i.e., the state with the highest average result obtained from simulations. Monte Carlo Tree Search, often abbreviated as MCTS, is a best-first, rollout- based tree search algorithm that has gained popularity in the field of artificial intelligence. This heuristic search algorithm starts by simulating a random game to the very end, and then updates the value of the played moves based on the game's result. This process is repeated many times, each time building a tree of explored game states. When deciding on the actual move to play, MCTS chooses the move that leads to the most promising state, i.e., the state with the highest average result over the simulations. MCTS has been successfully applied in many domains, including the game of Go, where it was a key component of DeepMind's AlphaGo program that defeated the world champion. The algorithm falls under the category of reinforcement learning, as it learns from experience by updating the values of the moves played based on the game's outcome. Monte Carlo Tree Search: Use Cases & Examples Monte Carlo Tree Search (MCTS) is a best-first, rollout-based tree search algorithm. In a given state of the game, MCTS starts by simulating a random game to the very end, then updates the value of the played moves based on the game's result. This process is repeated many times, each time building a tree of explored game states. When deciding on the actual move to play, MCTS chooses the move that leads to the most promising state, i.e., the state with the highest average result over the simulations. MCTS has been used successfully in many domains, perhaps most famously in the game of Go, where it was a key component of DeepMind's AlphaGo program that defeated the world champion. But MCTS has also been used in other games such as chess, shogi, and poker, as well as in other domains such as robotics, scheduling, and traffic control. In robotics, MCTS has been used for motion planning and control, where the robot has to navigate through an environment while avoiding obstacles. In scheduling, MCTS has been used to optimize the scheduling of jobs in a factory, leading to increased efficiency and reduced costs. In traffic control, MCTS has been used to optimize the timing of traffic lights at intersections, leading to reduced congestion and improved traffic flow. MCTS is a type of heuristic search and can be combined with reinforcement learning to further improve its performance. Reinforcement learning can be used to learn the values of the game states, which can then be used by MCTS to guide its search towards more promising states.

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