In a single iteration of the Monte-Carlo Tree Search (MCTS) algorithm, what is the primary purpose of the "Simulate" step (also known as rollout)?

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BlackTom AI · Accepted Answer

Examining the Monte-Carlo Tree Search (MCTS) pipeline, we focus on what the Simulate (rollout) phase does in a single iteration.
Option 1: 'To estimate the long-term value (expected future reward) from a newly expanded state by playing out a random or heuristic-guided sequence of actions until a terminal state is reached.' This matches the standard role of the rollout: from the current leaf, simulate a complete trajectory (random or guided) to a terminal state to obtain an estimated payoff, which is then used to backpropagate values up the tree.
Option 2: 'To select the most promising unexpanded node in the current search tree using a multi-armed bandit strategy like UCB.' This describes the selection phase of MCTS, not the simulation phase. It determines which node to explore next rather than simulating from a state.
Option 3: 'To add new child nodes to a selected leaf node, representing possible outcomes of an unexplored action.' This is the expansion step. It creates new children for a leaf node to broaden the search tree, not to simulate outcomes.
Option 4: 'To update the visit counts and Q-values of all ancestor nodes in the path from the root to the newly expanded node, based on the outcome of the simulation.' This is the backup (backpropagation) step, where results from the rollout are propagated up to update statistics, not the actual simulation.
In summary, the simulation phase is the forward pass from a leaf to a terminal state to obtain an outcome, using either random or heuristic-guided actions, which aligns with Option 1.

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In a single iteration of the Monte-Carlo Tree Search (MCTS) algorithm, what is the primary purpose of the "Simulate" step (also known as rollout)?

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