Select all of the following statements that are true about Monte Carlo Tree Search (MCTS)

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BlackTom AI · Accepted Answer

We start by restating the question and the given options to set the stage for analysis.
Question: Select all of the following statements that are true about Monte Carlo Tree Search (MCTS)
Answer options:
- MCTS works only for episodic tasks
- MCTS does planning at decision time
- MCTS can be used with a random rollout policy
- MCTS can be used with a non-random rollout policy
- The intent of MCTS is to select an action better than that of the underlying rollout policy
- MCTS does planning in the background

Now, let’s evaluate each statement one by one.
Option 1: "MCTS works only for episodic tasks" — This is not correct. MCTS is a search/planning algorithm that can be applied to many domains, including both episodic and continuing tasks, though it is commonly analyzed in episodic settings for simplicity. Restricting it to episodic tasks is an overly narrow and inaccurate claim.

Option 2: "MCTS does planning at decision time" — This statement is true. The core idea of MCTS is to perform planning during the decision process by iteratively building and exploring a search tree from the current state, using simulations (rollouts) to evaluate actions and guide future expansion.

Option 3: "MCTS can be used with a random rollout policy" — This statement is true. A rollout (simulation) policy is used to sample outcomes from leaf nodes; a random rollout is a common, simple choice, though not the only one.

Option 4: "MCTS can be used with a non-random rollout policy" — This statement is true. In practice, more informed rollout policies (e.g., heuristic or domain-specific policies) can be employed to produce better evaluations of leaf states and improve performance.

Option 5: "The intent of MCTS is to select an action better than that of the underlying rollout policy" — This statement is true. A primary motivation of MCTS is to improve over the default rollout policy by balancing exploration and exploitation to identify actions that outperform the baseline policy used during rollouts.

Option 6: "MCTS does planning in the background" — This statement is not accurate in the standard description. MCTS planning occurs at decision time, as the current state is being considered; it is not typically described as ongoing background planning separate from the decision moment. While one could run MCTS in parallel or precompute in some setups, the canonical definition emphasizes decision-time planning.

In summary, the true statements are options 2, 3, 4, and 5. The false statements are options 1 and 6. The reasoning above connects each option to the fundamental concepts of Monte Carlo Tree Search, including decision-time planning, the role of rollout policies (random or non-random), and the objective to improve upon the rollout policy’s action selection.

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Select all of the following statements that are true about Monte Carlo Tree Search (MCTS)

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