19,883 research outputs found
A Survey of Monte Carlo Tree Search Methods
Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work
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Department of Computer Science and EngineeringRecently deep reinforcement learning (DRL) algorithms show super human performances in the simulated game domains. In practical points, the sample efficiency is also one of the most important measures to determine the performance of a model. Especially for the environment of large search spaces (e.g. continuous action space), it is very critical condition to achieve the state-of-the-art performance.
In this thesis, we design a model to be applicable to multi-end games in continuous space with high sample efficiency. A multi-end game has several sub-games which are independent each other but affect the result of the game by some rules of its domain. We verify the algorithm in the environment of simulated curling.clos
Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar
Automatic machine learning is an important problem in the forefront of
machine learning. The strongest AutoML systems are based on neural networks,
evolutionary algorithms, and Bayesian optimization. Recently AlphaD3M reached
state-of-the-art results with an order of magnitude speedup using reinforcement
learning with self-play. In this work we extend AlphaD3M by using a pipeline
grammar and a pre-trained model which generalizes from many different datasets
and similar tasks. Our results demonstrate improved performance compared with
our earlier work and existing methods on AutoML benchmark datasets for
classification and regression tasks. In the spirit of reproducible research we
make our data, models, and code publicly available.Comment: ICML Workshop on Automated Machine Learnin
Assessing the Potential of Classical Q-learning in General Game Playing
After the recent groundbreaking results of AlphaGo and AlphaZero, we have
seen strong interests in deep reinforcement learning and artificial general
intelligence (AGI) in game playing. However, deep learning is
resource-intensive and the theory is not yet well developed. For small games,
simple classical table-based Q-learning might still be the algorithm of choice.
General Game Playing (GGP) provides a good testbed for reinforcement learning
to research AGI. Q-learning is one of the canonical reinforcement learning
methods, and has been used by (Banerjee Stone, IJCAI 2007) in GGP. In this
paper we implement Q-learning in GGP for three small-board games (Tic-Tac-Toe,
Connect Four, Hex)\footnote{source code: https://github.com/wh1992v/ggp-rl}, to
allow comparison to Banerjee et al.. We find that Q-learning converges to a
high win rate in GGP. For the -greedy strategy, we propose a first
enhancement, the dynamic algorithm. In addition, inspired by (Gelly
Silver, ICML 2007) we combine online search (Monte Carlo Search) to
enhance offline learning, and propose QM-learning for GGP. Both enhancements
improve the performance of classical Q-learning. In this work, GGP allows us to
show, if augmented by appropriate enhancements, that classical table-based
Q-learning can perform well in small games.Comment: arXiv admin note: substantial text overlap with arXiv:1802.0594
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