Incentive Learning in Monte Carlo Tree Search

Kao, Kuo-Yuan; Wu, I‐Chen; Yen, Shi-Jim; Shan, Yi-Chang

doi:10.1109/tciaig.2013.2248086

Cited by 6 publications

(2 citation statements)

References 13 publications

(18 reference statements)

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“…The classical RAVE approach (see Sect. 2.2) has been extended by Kao et al (2013). They introduce the RIDE method (Rapid Incentive Difference Evaluation) where the default MCTS policy is updated by using differences (9) between action values for the same state s.…”

Section: Policy Updatementioning

confidence: 99%

Monte Carlo Tree Search: a review of recent modifications and applications

Świechowski¹,

et al. 2022

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Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots or solving sequential decision problems. The method relies on intelligent tree search that balances exploration and exploitation. MCTS performs random sampling in the form of simulations and stores statistics of actions to make more educated choices in each subsequent iteration. The method has become a state-of-the-art technique for combinatorial games. However, in more complex games (e.g. those with a high branching factor or real-time ones) as well as in various practical domains (e.g. transportation, scheduling or security) an efficient MCTS application often requires its problem-dependent modification or integration with other techniques. Such domain-specific modifications and hybrid approaches are the main focus of this survey. The last major MCTS survey was published in 2012. Contributions that appeared since its release are of particular interest for this review.

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Section: Policy Updatementioning

confidence: 99%

Monte Carlo Tree Search: a review of recent modifications and applications

Świechowski¹,

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The classical RAVE approach (see Section 2.2) has been extended by Kao et al (2013). They introduce the RIDE method (Rapid Incentive Difference Evaluation) where the default MCTS policy is updated by using differences (9) between action values for the same state s.…”

Section: Policy Updatementioning

confidence: 99%

Monte Carlo Tree Search: A Review of Recent Modifications and Applications

Świechowski,

Godlewski,

Sawicki

et al. 2021

Preprint

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Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots or solving sequential decision problems. The method relies on intelligent tree search that balances exploration and exploitation. MCTS performs random sampling in the form of simulations and stores statistics of actions to make more educated choices in each subsequent iteration. The method has become a state-ofthe-art technique for combinatorial games, however, in more complex games (e.g. those with high branching factor or real-time ones), as well as in various practical domains (e.g. transportation, scheduling or security) an efficient MCTS application often requires its problemdependent modification or integration with other techniques. Such domain-specific modifications and hybrid approaches are the main focus of this survey. The last major MCTS survey has been published in 2012. Contributions that appeared since its release are of particular interest for this review.

show abstract