Pruning Game Tree by Rollouts

@inproceedings{Huang2015PruningGT,
  title={Pruning Game Tree by Rollouts},
  author={Bojun Huang},
  booktitle={AAAI},
  year={2015}
}
  • Bojun Huang
  • Published in AAAI 25 January 2015
  • Computer Science
In this paper we show that the α-β algorithm and its successor MT-SSS*, as two classic minimax search algorithms, can be implemented as rollout algorithms, a generic algorithmic paradigm widely used in many domains. [] Key Method We show that any rollout policy in this family (either deterministic or randomized) is guaranteed to evaluate the game tree correctly with a finite number of rollouts. Moreover, we identify simple rollout policies in this family that "implement" α-β and MT-SSS*. Specifically, given…

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References

SHOWING 1-10 OF 38 REFERENCES

Rollout-based Game-tree Search Outprunes Traditional Alpha-beta

This paper modifications a rollout-based method, FSSS, to allow for use in game-tree search and shows it outprunes alpha-beta both empirically and formally.

A Minimax Algorithm Better than Alpha-Beta?

Best-First Fixed-Depth Minimax Algorithms

A new formulation for Stockman's SSS ∗ algorithm, based on Alpha-Beta, is presented, finally transforming it into a practical algorithm, and a framework that facilitates the construction of several best-first fixed-depth game-tree search algorithms, known and new is presented.

Monte-Carlo Tree Search and minimax hybrids

This paper proposes M CTS-minimax hybrids that employ shallow minimax searches within the MCTS framework and investigates their effectiveness in the test domains of Connect-4 and Breakthrough.

A Review of Game-Tree Pruning

  • T. Marsland
  • Computer Science
    J. Int. Comput. Games Assoc.
  • 1986
These essential parts of game-tree searching and pruning are reviewed here, and the performance of refinements, such as aspiration and principal variation search, and aids like transposition and history tables are compared.

Score Bounded Monte-Carlo Tree Search

The proposed algorithm improves significantly a MCTS solver to take into account bounds on the possible scores of a node in order to select the nodes to explore in games that can end in draw positions.

On-Line Search for Solving Markov Decision Processes via Heuristic Sampling

This paper investigates the problem of refining near optimal policies via online search techniques, tackling the local problem of finding an optimal action for a single current state of the system, and considers an on-line approach based on sampling: at each step, a randomly sampled look-ahead tree is developed to compute the optimalaction for the current state.

Finite-time Analysis of the Multiarmed Bandit Problem

This work shows that the optimal logarithmic regret is also achievable uniformly over time, with simple and efficient policies, and for all reward distributions with bounded support.

Monte Carlo *-Minimax Search

This paper introduces Monte Carlo *-Minimax Search (MCMS), a Monte Carlo search algorithm for turned-based, stochastic, two-player, zero-sum games of perfect information. The algorithm is designed