GitPlanet

Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details
All Projects → benedekrozemberczki → Awesome Monte Carlo Tree Search Papers

benedekrozemberczki / Awesome Monte Carlo Tree Search Papers

Licence: cc0-1.0
A curated list of Monte Carlo tree search papers with implementations.

Programming Languages

python
139335 projects - #7 most used programming language

Labels

deep-learningmachine-learningreinforcement-learninglearningmachine-learning-algorithmsrlpolicy-gradientq-learning

Projects that are alternatives of or similar to Awesome Monte Carlo Tree Search Papers

Learningx
Deep & Classical Reinforcement Learning + Machine Learning Examples in Python
Stars: ✭ 241 (-37.73%)
Mutual labels:  learning, reinforcement-learning, machine-learning-algorithms, q-learning
Reinforcement learning tutorial with demo
Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..
Stars: ✭ 442 (+14.21%)
Mutual labels:  reinforcement-learning, policy-gradient, q-learning
Reinforcement Learning With Tensorflow
Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学
Stars: ✭ 6,948 (+1695.35%)
Mutual labels:  reinforcement-learning, policy-gradient, q-learning
Hands On Reinforcement Learning With Python
Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow
Stars: ✭ 640 (+65.37%)
Mutual labels:  reinforcement-learning, policy-gradient, q-learning
Easy Rl
强化学习中文教程，在线阅读地址：https://datawhalechina.github.io/easy-rl/
Stars: ✭ 3,004 (+676.23%)
Mutual labels:  reinforcement-learning, policy-gradient, q-learning
Pytorch Rl
Tutorials for reinforcement learning in PyTorch and Gym by implementing a few of the popular algorithms. [IN PROGRESS]
Stars: ✭ 121 (-68.73%)
Mutual labels:  reinforcement-learning, rl, policy-gradient
Rl trading
An environment to high-frequency trading agents under reinforcement learning
Stars: ✭ 205 (-47.03%)
Mutual labels:  reinforcement-learning, rl, q-learning
Amazon Sagemaker Examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Stars: ✭ 6,346 (+1539.79%)
Mutual labels:  learning, reinforcement-learning, rl
Cherry
A PyTorch Library for Reinforcement Learning Research
Stars: ✭ 143 (-63.05%)
Mutual labels:  learning, reinforcement-learning, rl
Gym Gazebo2
gym-gazebo2 is a toolkit for developing and comparing reinforcement learning algorithms using ROS 2 and Gazebo
Stars: ✭ 257 (-33.59%)
Mutual labels:  reinforcement-learning, rl
Matterport3dsimulator
AI Research Platform for Reinforcement Learning from Real Panoramic Images.
Stars: ✭ 260 (-32.82%)
Mutual labels:  reinforcement-learning, rl
Rad
RAD: Reinforcement Learning with Augmented Data
Stars: ✭ 268 (-30.75%)
Mutual labels:  reinforcement-learning, rl
RL-2018
Reinforcement Learning at UCLA IPAM RIPS 2018
Stars: ✭ 15 (-96.12%)
Mutual labels:  machine-learning-algorithms, q-learning
Paddle-RLBooks
Paddle-RLBooks is a reinforcement learning code study guide based on pure PaddlePaddle.
Stars: ✭ 113 (-70.8%)
Mutual labels:  q-learning, policy-gradient
Opennars
OpenNARS for Research 3.0+
Stars: ✭ 264 (-31.78%)
Mutual labels:  learning, rl
Explorer
Explorer is a PyTorch reinforcement learning framework for exploring new ideas.
Stars: ✭ 54 (-86.05%)
Mutual labels:  q-learning, policy-gradient
Trading Bot
Stock Trading Bot using Deep Q-Learning
Stars: ✭ 273 (-29.46%)
Mutual labels:  reinforcement-learning, q-learning
Dinoruntutorial
Accompanying code for Paperspace tutorial "Build an AI to play Dino Run"
Stars: ✭ 285 (-26.36%)
Mutual labels:  reinforcement-learning, q-learning
DRL in CV
A course on Deep Reinforcement Learning in Computer Vision. Visit Website:
Stars: ✭ 59 (-84.75%)
Mutual labels:  q-learning, policy-gradient
Drq
DrQ: Data regularized Q
Stars: ✭ 268 (-30.75%)
Mutual labels:  reinforcement-learning, rl
View All Similar Projects ➔

Awesome Monte Carlo Tree Search Papers.

Awesome PRs Welcomerepo size License⠀benedekrozemberczki⠀⠀

A curated list of Monte Carlo tree search papers with implementations from the following conferences/journals:

Similar collections about graph classification, gradient boosting, classification/regression trees, fraud detection, and community detection papers with implementations.

2020

  • Monte Carlo Tree Search in Continuous Spaces Using Voronoi Optimistic Optimization with Regret Bounds (AAAI 2020)

    • Beomjoon Kim, Kyungjae Lee, Sungbin Lim, Leslie Pack Kaelbling, Tomás Lozano-Pérez
    • [Paper]

  • Neural Architecture Search Using Deep Neural Networks and Monte Carlo Tree Search (AAAI 2020)

    • Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca
    • [Paper]
    • [Code]

  • Monte-Carlo Tree Search in Continuous Action Spaces with Value Gradients (AAAI 2020)

    • Jongmin Lee, Wonseok Jeon, Geon-Hyeong Kim, Kee-Eung Kim
    • [Paper]
    • [Code]

  • Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions (AISTATS 2020)

    • Lars Buesing, Nicolas Heess, Theophane Weber
    • [Paper]

  • Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search (ICLR 2020)

    • Anji Liu, Jianshu Chen, Mingze Yu, Yu Zhai, Xuewen Zhou, Ji Liu
    • [Paper]
    • [Code]

  • Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains (ICML 2020)

  • Sub-Goal Trees a Framework for Goal-Based Reinforcement Learning (ICML 2020)

    • Tom Jurgenson, Or Avner, Edward Groshev, Aviv Tamar
    • [Paper]

  • Monte-Carlo Tree Search for Scalable Coalition Formation (IJCAI 2020)

    • Feng Wu, Sarvapali D. Ramchurn
    • [Paper]

  • Generalized Mean Estimation in Monte-Carlo Tree Search (IJCAI 2020)

    • Tuan Dam, Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen
    • [Paper]

  • Sparse Tree Search Optimality Guarantees in POMDPs with Continuous Observation Spaces (IJCAI 2020)

    • Michael H. Lim, Claire Tomlin, Zachary N. Sunberg
    • [Paper]

  • Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions (NeurIPS 2020)

    • Matthew Faw, Rajat Sen, Karthikeyan Shanmugam, Constantine Caramanis, Sanjay Shakkottai
    • [Paper]

  • Extracting Knowledge from Web Text with Monte Carlo Tree Search (WWW 2020)

    • Guiliang Liu, Xu Li, Jiakang Wang, Mingming Sun, Ping Li
    • [Paper]

2019

  • ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search (AAAI 2019)

  • A Monte Carlo Tree Search Player for Birds of a Feather Solitaire (AAAI 2019)

  • Vine Copula Structure Learning via Monte Carlo Tree Search (AISTATS 2019)

  • Noisy Blackbox Optimization using Multi-fidelity Queries: A Tree Search Approach (AISTATS 2019)

    • Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai
    • [Paper]
    • [Code]

  • Reinforcement Learning Based Monte Carlo Tree Search for Temporal Path Discovery (ICDM 2019)

    • Pengfei Ding, Guanfeng Liu, Pengpeng Zhao, An Liu, Zhixu Li, Kai Zheng
    • [Paper]

  • Monte Carlo Tree Search for Policy Optimization (IJCAI 2019)

    • Xiaobai Ma, Katherine Rose Driggs-Campbell, Zongzhang Zhang, Mykel J. Kochenderfer
    • [Paper]

  • Subgoal-Based Temporal Abstraction in Monte-Carlo Tree Search (IJCAI 2019)

    • Thomas Gabor, Jan Peter, Thomy Phan, Christian Meyer, Claudia Linnhoff-Popien
    • [Paper]
    • [Code]

  • Automated Machine Learning with Monte-Carlo Tree Search (IJCAI 2019)

    • Herilalaina Rakotoarison, Marc Schoenauer, Michèle Sebag
    • [Paper]
    • [Code]

  • Multiple Policy Value Monte Carlo Tree Search (IJCAI 2019)

    • Li-Cheng Lan, Wei Li, Ting-Han Wei, I-Chen Wu
    • [Paper]

  • Learning Compositional Neural Programs with Recursive Tree Search and Planning (NeurIPS 2019)

    • Thomas Pierrot, Guillaume Ligner, Scott E. Reed, Olivier Sigaud, Nicolas Perrin, Alexandre Laterre, David Kas, Karim Beguir, Nando de Freitas
    • [Paper]

2018

  • Monte Carlo Methods for the Game Kingdomino (CIG 2018)

  • Reset-free Trial-and-Error Learning for Robot Damage Recovery (RAS 2018)

  • Memory-Augmented Monte Carlo Tree Search (AAAI 2018)

    • Chenjun Xiao, Jincheng Mei, Martin Müller
    • [Paper]

  • Feedback-Based Tree Search for Reinforcement Learning (ICML 2018)

    • Daniel R. Jiang, Emmanuel Ekwedike, Han Liu
    • [Paper]

  • Extended Increasing Cost Tree Search for Non-Unit Cost Domains (IJCAI 2018)

    • Thayne T. Walker, Nathan R. Sturtevant, Ariel Felner
    • [Paper]

  • Three-Head Neural Network Architecture for Monte Carlo Tree Search (IJCAI 2018)

    • Chao Gao, Martin Müller, Ryan Hayward
    • [Paper]

  • Bidding in Periodic Double Auctions Using Heuristics and Dynamic Monte Carlo Tree Search (IJCAI 2018)

    • Moinul Morshed Porag Chowdhury, Christopher Kiekintveld, Son Tran, William Yeoh
    • [Paper]

  • Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search (NIPS 2018)

    • Zhuwen Li, Qifeng Chen, Vladlen Koltun
    • [Paper]

  • M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search (NIPS 2018)

    • Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, Jianfeng Gao
    • [Paper]

  • Single-Agent Policy Tree Search With Guarantees (NIPS 2018)

    • Laurent Orseau, Levi Lelis, Tor Lattimore, Theophane Weber
    • [Paper]

  • Monte-Carlo Tree Search for Constrained POMDPs (NIPS 2018)

    • Jongmin Lee, Geon-hyeong Kim, Pascal Poupart, Kee-Eung Kim
    • [Paper]

2017

  • An Analysis of Monte Carlo Tree Search (AAAI 2017)

    • Steven James, George Dimitri Konidaris, Benjamin Rosman
    • [Paper]

  • Beyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural Network and Long-Term Evaluation (AAAI 2017)

    • Jinzhuo Wang, Wenmin Wang, Ronggang Wang, Wen Gao
    • [Paper]

  • Designing Better Playlists with Monte Carlo Tree Search (AAAI 2017)

    • Elad Liebman, Piyush Khandelwal, Maytal Saar-Tsechansky, Peter Stone
    • [Paper]

  • Learning in POMDPs with Monte Carlo Tree Search (ICML 2017)

    • Sammie Katt, Frans A. Oliehoek, Christopher Amato
    • [Paper]

  • Learning to Run Heuristics in Tree Search (IJCAI 2017)

    • Elias B. Khalil, Bistra Dilkina, George L. Nemhauser, Shabbir Ahmed, Yufen Shao
    • [Paper]

  • Estimating the Size of Search Trees by Sampling with Domain Knowledge (IJCAI 2017)

    • Gleb Belov, Samuel Esler, Dylan Fernando, Pierre Le Bodic, George L. Nemhauser
    • [Paper]

  • A Monte Carlo Tree Search Approach to Active Malware Analysis (IJCAI 2017)

    • Riccardo Sartea, Alessandro Farinelli
    • [Paper]

  • Monte-Carlo Tree Search by Best Arm Identification (NIPS 2017)

    • Emilie Kaufmann, Wouter M. Koolen
    • [Paper]

  • Thinking Fast and Slow with Deep Learning and Tree Search (NIPS 2017)

    • Thomas Anthony, Zheng Tian, David Barber
    • [Paper]

  • Monte-Carlo Tree Search using Batch Value of Perfect Information (UAI 2017)

    • Shahaf S. Shperberg, Solomon Eyal Shimony, Ariel Felner
    • [Paper]

2016

  • Using Domain Knowledge to Improve Monte-Carlo Tree Search Performance in Parameterized Poker Squares (AAAI 2016)

    • Robert Arrington, Clay Langley, Steven Bogaerts
    • [Paper]

  • Monte Carlo Tree Search for Multi-Robot Task Allocation (AAAI 2016)

    • Bilal Kartal, Ernesto Nunes, Julio Godoy, Maria L. Gini
    • [Paper]

  • Large Scale Hard Sample Mining with Monte Carlo Tree Search (CVPR 2016)

    • Olivier Canévet, François Fleuret
    • [Paper]

  • On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search (ICML 2016)

    • Piyush Khandelwal, Elad Liebman, Scott Niekum, Peter Stone
    • [Paper]

  • Deep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games (IJCAI 2016)

    • Xiaoxiao Guo, Satinder P. Singh, Richard L. Lewis, Honglak Lee
    • [Paper]

  • Monte Carlo Tree Search in Continuous Action Spaces with Execution Uncertainty (IJCAI 2016)

    • Timothy Yee, Viliam Lisý, Michael H. Bowling
    • [Paper]

  • Learning Predictive State Representations via Monte-Carlo Tree Search (IJCAI 2016)

    • Yunlong Liu, Hexing Zhu, Yifeng Zeng, Zongxiong Dai
    • [Paper]

2015

  • Efficient Globally Optimal Consensus Maximisation with Tree Search (CVPR 2015)

    • Tat-Jun Chin, Pulak Purkait, Anders P. Eriksson, David Suter
    • [Paper]

  • Interplanetary Trajectory Planning with Monte Carlo Tree Search (IJCAI 2015)

    • Daniel Hennes, Dario Izzo
    • [Paper]

2014

  • State Aggregation in Monte Carlo Tree Search (AAAI 2014)

    • Jesse Hostetler, Alan Fern, Tom Dietterich
    • [Paper]

  • Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning (NIPS 2014)

    • Xiaoxiao Guo, Satinder P. Singh, Honglak Lee, Richard L. Lewis, Xiaoshi Wang
    • [Paper]

  • Learning Partial Policies to Speedup MDP Tree Search (UAI 2014)

2013

  • Monte Carlo Tree Search for Scheduling Activity Recognition (ICCV 2013)

    • Mohamed R. Amer, Sinisa Todorovic, Alan Fern, Song-Chun Zhu
    • [Paper]

  • Convergence of Monte Carlo Tree Search in Simultaneous Move Games (NIPS 2013)

    • Viliam Lisý, Vojtech Kovarík, Marc Lanctot, Branislav Bosanský
    • [Paper]

  • Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search (NIPS 2013)

    • Aijun Bai, Feng Wu, Xiaoping Chen
    • [Paper]

2012

  • Generalized Monte-Carlo Tree Search Extensions for General Game Playing (AAAI 2012)

2011

  • A Local Monte Carlo Tree Search Approach in Deterministic Planning (AAAI 2011)

    • Fan Xie, Hootan Nakhost, Martin Müller
    • [Paper]

  • Real-Time Solving of Quantified CSPs Based on Monte-Carlo Game Tree Search (IJCAI 2011)

    • Satomi Baba, Yongjoon Joe, Atsushi Iwasaki, Makoto Yokoo
    • [Paper]

  • Nested Rollout Policy Adaptation for Monte Carlo Tree Search (IJCAI 2011)

  • Variance Reduction in Monte-Carlo Tree Search (NIPS 2011)

    • Joel Veness, Marc Lanctot, Michael H. Bowling
    • [Paper]

  • Learning Is Planning: Near Bayes-Optimal Reinforcement Learning via Monte-Carlo Tree Search (UAI 2011)

    • John Asmuth, Michael L. Littman
    • [Paper]

2010

  • Understanding the Success of Perfect Information Monte Carlo Sampling in Game Tree Search (AAAI 2010)

    • Jeffrey Richard Long, Nathan R. Sturtevant, Michael Buro, Timothy Furtak
    • [Paper]

  • Bayesian Inference in Monte-Carlo Tree Search (UAI 2010)

    • Gerald Tesauro, V. T. Rajan, Richard Segal
    • [Paper]

2009

  • Monte Carlo Tree Search Techniques in the Game of Kriegspiel (IJCAI 2009)

    • Paolo Ciancarini, Gian Piero Favini
    • [Paper]

  • Bootstrapping from Game Tree Search (NIPS 2009)

    • Joel Veness, David Silver, William T. B. Uther, Alan Blair
    • [Paper]

2008

  • Direct Mining of Discriminative and Essential Frequent Patterns via Model-Based Search Tree (KDD 2008)
    • Wei Fan, Kun Zhang, Hong Cheng, Jing Gao, Xifeng Yan, Jiawei Han, Philip S. Yu, Olivier Verscheure
    • [Paper]

2007

  • Bandit Algorithms for Tree Search (UAI 2007)
    • Pierre-Arnaud Coquelin, Rémi Munos
    • [Paper]

2006

  • Properties of Forward Pruning in Game-Tree Search (AAAI 2006)

  • Graph Branch Algorithm: An Optimum Tree Search Method for Scored Dependency Graph with Arc Co-Occurrence Constraints (ACL 2006)

2005

  • Game-Tree Search with Combinatorially Large Belief States (IJCAI 2005)
    • Austin Parker, Dana S. Nau, V. S. Subrahmanian
    • [Paper]

2003

  • Solving Finite Domain Constraint Hierarchies by Local Consistency and Tree Search (IJCAI 2003)
    • Stefano Bistarelli, Philippe Codognet, Kin Chuen Hui, Jimmy Ho-Man Lee
    • [Paper]

2001

  • Incomplete Tree Search using Adaptive Probing (IJCAI 2001)

1998

  • KnightCap: A Chess Programm That Learns by Combining TD with Game-Tree Search (ICML 1998)
    • Jonathan Baxter, Andrew Tridgell, Lex Weaver
    • [Paper]

1988

  • A Tree Search Algorithm for Target Detection in Image Sequences (CVPR 1988)
    • Steven D. Blostein, Thomas S. Huang
    • [Paper]

License

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].