Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange
نویسندگان
چکیده
منابع مشابه
Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange
Reciprocating interactions represent a central feature of all human exchanges. They have been the target of various recent experiments, with healthy participants and psychiatric populations engaging as dyads in multi-round exchanges such as a repeated trust task. Behaviour in such exchanges involves complexities related to each agent's preference for equity with their partner, beliefs about the...
متن کاملHierarchical Monte-Carlo Planning
Monte-Carlo Tree Search, especially UCT and its POMDP version POMCP, have demonstrated excellent performance on many problems. However, to efficiently scale to large domains one should also exploit hierarchical structure if present. In such hierarchical domains, finding rewarded states typically requires to search deeply; covering enough such informative states very far from the root becomes co...
متن کاملBandit Based Monte-Carlo Planning
For large state-space Markovian Decision Problems MonteCarlo planning is one of the few viable approaches to find near-optimal solutions. In this paper we introduce a new algorithm, UCT, that applies bandit ideas to guide Monte-Carlo planning. In finite-horizon or discounted MDPs the algorithm is shown to be consistent and finite sample bounds are derived on the estimation error due to sampling...
متن کاملAn Approach in Radiation Therapy Treatment Planning: A Fast, GPU-Based Monte Carlo Method
Introduction: An accurate and fast radiation dose calculation is essential for successful radiation radiotherapy. The aim of this study was to implement a new graphic processing unit (GPU) based radiation therapy treatment planning for accurate and fast dose calculation in radiotherapy centers. Materials and Methods: A program was written for parallel runnin...
متن کاملThe Parallelization of Monte-Carlo Planning - Parallelization of MC-Planning
Since their impressive successes in various areas of large-scale parallelization, recent techniques like UCT and other Monte-Carlo planning variants (Kocsis and Szepesvari, 2006a) have been extensively studied (Coquelin and Munos, 2007; Wang and Gelly, 2007). We here propose and compare various forms of parallelization of bandit-based tree-search, in particular for our computer-go algorithm XYZ.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLOS Computational Biology
سال: 2015
ISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1004254