Strategy research based on chess shapes for Tibetan JIU computer game

Li, Xiali; Wang, Song; Lv, Zhengyu; Li, Yongji; Wu, Licheng

doi:10.3233/icg-180058

Cited by 5 publications

(10 citation statements)

References 4 publications

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“…However, Busoniuetal. [16] clearly pointed out that Improved Multi-Agent Reinforcement Learning (IMARL) still facing some challenges, (i.e,) the problem in curse of dimensionality and the instability of the environment becoming more serious. Also, the multi-objective optimization problem of signal timing, some heuristic or metaheuristic methods were adopted to deal with it.…”

Section: Fig2 Dynamic Crowd Evacuationmentioning

confidence: 99%

Improved Particle Swam Optimization for Crowd Simulation Using Hybrid Agent Reinforcement Learning Algorithm

Pavithra¹,

Radhamani²,

Thangavel³

2021

ITII

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In an emergency route planning technique, simulating the dynamic crowd has route capacity constraints and global target of evacuating all crowd evacuees. To stimulate the crowd, the new arena is developed to know the real-time situation to face the crowd evacuation on exit point. The crowd evacuation is done with the process of Hybrid Agent Reinforcement Learning (HARL) algorithm consisting of Improved Multi-Agent Reinforcement Learning (IMARL) and State-Action-Reward-State-Action (SARSA). In the proposed work, the appropriate route selection mechanism focused on finding optimum evacuation route(s) is done in the first phase. Dynamic crowd can also be evacuated to find its way with the support of the HARL process in the second phase. The proposed HARL method can also be implemented with multi-objective improved particle swarm optimization (IPSO) technique for crowd simulation. The experimented results demonstrate the effectiveness of stability in the HARL process, which provides an improved performance for crowd simulation

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Section: Fig2 Dynamic Crowd Evacuationmentioning

confidence: 99%

Improved Particle Swam Optimization for Crowd Simulation Using Hybrid Agent Reinforcement Learning Algorithm

Pavithra¹,

Radhamani²,

Thangavel³

2021

ITII

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“…e goal in the battle stage is moving or capturing stones until one player wins the game. e movements and capturing methods are similar to those of international checkers [1,2].…”

Section: Tibetan Jiu Chess Rulesmentioning

confidence: 99%

“…e feedback function for a time difference (TD) algorithm based on important shapes [1,2] is constructed for the battle stage. ResNet18 structure is improved to be suitable for the deep neural network training considering its lower error rate and better performance in image classification [3].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Online and Offline Reinforcement Learning for Tibetan Jiu Chess

et al. 2020

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In this study, hybrid state-action-reward-state-action (SARSAλ) and Q-learning algorithms are applied to different stages of an upper confidence bound applied to tree search for Tibetan Jiu chess. Q-learning is also used to update all the nodes on the search path when each game ends. A learning strategy that uses SARSAλ and Q-learning algorithms combining domain knowledge for a feedback function for layout and battle stages is proposed. An improved deep neural network based on ResNet18 is used for self-play training. Experimental results show that hybrid online and offline reinforcement learning with a deep neural network can improve the game program’s learning efficiency and understanding ability for Tibetan Jiu chess.

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“…There are few Studies on JIU chess computer games [13]. In [15], classic chess shapes were designed for JIU chess using expert knowledge. Each chess shape was weighted according to the JIU game play.…”

Section: Related Workmentioning

confidence: 99%

“…Chess power of all existing JIU playing engines based on those studies is low. [13]- [15]. Therefore, to improve the computer game power of JIU chess, we propose a temporal-difference algorithm-based reinforcement learning model for the preparation stage of JIU chess.…”

Section: Introductionmentioning

confidence: 99%

A Reinforcement Learning Model Based on Temporal Difference Algorithm

Wang

et al. 2019

IEEE Access

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In some sense, computer game can be used as a test bed of artificial intelligence to develop intelligent algorithms. The paper proposed a kind of intelligent method: a reinforcement learning model based on temporal difference (TD) algorithm. And then the method is used to improve the playing power of the computer game of a special kind of chess. JIU chess, also called Tibetan Go chess, is mainly played in places where Tibetan tribes gather. Its play process is divided two sequential stages: preparation and battle. The layout at preparation is vital for the successive battle, even for the final winning. Studies on Tibetan JIU chess have focused on Bayesian network based pattern extraction and chess shape based strategy, which do not perform well. To address the low chess power of JIU chess from the view of artificial intelligence, we developed a reinforcement learning model based on temporal difference (TD) algorithm for the preparation stage of JIU. First, the search range was limited within a 6 × 6 area at the center of the chessboard, and the TD learning architecture was combined with chess shapes to construct an intelligent environmental feedback system. Second, optimal state transition strategies were obtained by self-play. In addition, the results of the reinforcement learning model were output as SGF files, which act as a pattern library for the battle stage. The experimental results demonstrate that this reinforcement learning model can effectively improve the playing strength of JIU program and outperform the other methods.INDEX TERMS Artificial intelligence, reinforcement learning, temporal difference algorithm, JIU chess.

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Strategy research based on chess shapes for Tibetan JIU computer game

Cited by 5 publications

References 4 publications

Improved Particle Swam Optimization for Crowd Simulation Using Hybrid Agent Reinforcement Learning Algorithm

Improved Particle Swam Optimization for Crowd Simulation Using Hybrid Agent Reinforcement Learning Algorithm

Hybrid Online and Offline Reinforcement Learning for Tibetan Jiu Chess

A Reinforcement Learning Model Based on Temporal Difference Algorithm

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