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Action Guidance with MCTS for Deep Reinforcement Learning

Action Guidance with MCTS for Deep Reinforcement Learning

25 July 2019
Bilal Kartal
Pablo Hernandez-Leal
Matthew E. Taylor
ArXivPDFHTML

Papers citing "Action Guidance with MCTS for Deep Reinforcement Learning"

4 / 4 papers shown
Title
Policy Guided Tree Search for Enhanced LLM Reasoning
Policy Guided Tree Search for Enhanced LLM Reasoning
Yang Li
LRM
53
0
0
04 Feb 2025
JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player
  Zero-Sum Games
JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games
Yang Li
Kun Xiong
Yingping Zhang
Jiangcheng Zhu
Stephen Marcus McAleer
Wei Pan
Jun Wang
Zonghong Dai
Yaodong Yang
39
2
0
09 Aug 2023
Monte Carlo Tree Search: A Review of Recent Modifications and
  Applications
Monte Carlo Tree Search: A Review of Recent Modifications and Applications
M. Świechowski
Konrad Godlewski
B. Sawicki
Jacek Mańdziuk
41
250
0
08 Mar 2021
Combining Q-Learning and Search with Amortized Value Estimates
Combining Q-Learning and Search with Amortized Value Estimates
Jessica B. Hamrick
V. Bapst
Alvaro Sanchez-Gonzalez
Tobias Pfaff
T. Weber
Lars Buesing
Peter W. Battaglia
OffRL
27
47
0
05 Dec 2019
1