A game strategy model in the digital curling system based on NFSP

Han, Yuntao; Zhou, Qibin; Duan, Fuqing

doi:10.1007/s40747-021-00345-6

Cited by 11 publications

(12 citation statements)

References 9 publications

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“…As we can see, we have a higher winning rate against the other two models because our Curling MCTS can explore new action spaces via separate alternative options. It is worth mentioning that our decisionmaking method has a lower winning rate when compared to NFSP [21]. This is because NFSP uses two adversary learning networks to produce supervised data automatically.…”

Section: Resultsmentioning

confidence: 97%

“…The KR-DL-UCT searched in continuous action space by kernel regression and trained by supervised learning and self-game reinforcement learning, which improved the speed and accuracy of constant Monte Carlo tree search and won the international digital curling competition. Han et al [21] combined NFSP and KR-UCT in a digital curling game. The NFSP uses two adversary learning networks to produce supervised data, and KR-UCT can be used for large game tree searching in continuous action space.…”

Section: Digital Curling Policy Recommendationmentioning

confidence: 99%

“…Since then, more and more works have started to use reinforcement learning for strategy analysis of curling. [19][20][21]. These methods are divided into two categories, one is deep reinforcement learning and the other is pure reinforcement learning.…”

Section: Introductionmentioning

confidence: 99%

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Policy decision of curling in real competition scenes

Xiao

Wang

et al. 2022

Complex Intell. Syst.

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Policy decision of curling refers to providing strategy suggestions for curling competition with the help of computers. Existing curling agents have achieved good results in the digital scenarios, but cannot make correct decisions when applied to actual competition and training scenes. In this paper, a strategies decision agent in the real scene has been proposed. The competition situation was acquired by a Situation-Aware Network and mapped by a Digital Extraction module. We designed Curling MCTS to explore the best strategy in continuous space. The effectiveness of our framework has been verified by experiments and evaluated by China’s wheelchair curling team at China Disabled Sports Management Center. With the help of our system, China’s wheelchair curling team trained effectively and won the championship in the XIII Paralympic Winter Games (2022, Beijing). In addition, a new curling target detection dataset was provided.

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Section: Resultsmentioning

confidence: 97%

Section: Digital Curling Policy Recommendationmentioning

confidence: 99%

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Policy decision of curling in real competition scenes

Xiao

Wang

et al. 2022

Complex Intell. Syst.

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“…Currently, deep reinforcement learning (DRL) has shown tremendous potential in solving complex decision-making problems. DRL has spurred a lot of significant breakthroughs in many applications, such as manipulator controlling [18], autonomous driving [19,20], and games [21,22]. Considering the strengths of DRL, researchers attempt to apply it to tackle the PointGoal navigation problem [4,[23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Safe-Nav: learning to prevent PointGoal navigation failure in unknown environments

Jin

Meng

Dai

et al. 2022

Complex Intell. Syst.

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Training robots to safely navigate (Safe-Nav) in uncertain complex environments using the RGB-D sensor is quite challenging as it involves the performance of different tasks such as obstacle avoidance, optimal path planning, and control. Traditional navigation approaches cannot generate suitable paths which guarantee enough visible features. Recent learning-based methods are still not mature enough due to their proneness to collisions and prohibitive computational cost. This paper focuses on generating safe trajectories to the desired goal while avoiding collisions and tracking failure in unknown complex environments. We present Safe-Nav, a hierarchical framework composed of the visual simultaneous localization and mapping (SLAM) module, the global planner module and the local planner module. The visual SLAM module generates the navigation map and the robot pose. The global planner module plans a local waypoint on the real-time navigation map. In the local planner module, a deep-reinforcement-learning-based (DRL-based) policy is presented for taking safe actions towards local waypoints. Our DRL-based policy can learn different navigation skills (e.g., avoiding collisions and avoiding tracking failure) through specialized modes without any supervisory signals when the PointGoal-navigation-specied reward is provided. We have demonstrated the performance of our proposed Safe-Nav in the Habitat simulation environment. Our approach outperforms the recent learning-based method and conventional navigation approach with relative improvements of over 205% (0.55 vs. 0.18) and 139% (0.55 vs. 0.23) in the success rate, respectively.

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“…The RL approach has been used to find out the game strategy in digital curling by reaching the Nash equilibrium [8]. The digital curling game is a two-player zero-sum extensive game in a continuous action space.…”

Section: Introductionmentioning

confidence: 99%

Game Strategy Analysis Methodology

Chindea¹,

Iosif²,

Elekes³

et al. 2022

RoCHI - International Conference on Human-Computer Interaction

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The video games are grouped in different categories such as Action, Adventure, Racing, Platform, Simulation, Role-Playing, etc. As an example, an Action game focuses on the interaction with users, they are challenging and require good skills from the users, such as reaction-time, hand-eye coordination. In Racing games, players participate in racing competitions, such as car races, etc. However, in most of the cases, video games combine aspects of different categories. RELATED WORKMany of the game strategies in which two competitors are in competition are solved by finding the Nash equilibrium, a notion defined and demonstrated in 1950 by the mathematician John Forbes Nash [1]. The Nash equilibrium describes a state of strategic equilibrium, in which a player

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A game strategy model in the digital curling system based on NFSP

Cited by 11 publications

References 9 publications

Policy decision of curling in real competition scenes

Policy decision of curling in real competition scenes

Safe-Nav: learning to prevent PointGoal navigation failure in unknown environments

Game Strategy Analysis Methodology

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