Abstract:
EinStein würfelt nicht! (EWN) is a perfect information stochastic game, in which randomness influences the game process enormously. In this article, we propose an optimiz...Show MoreMetadata
Abstract:
EinStein würfelt nicht! (EWN) is a perfect information stochastic game, in which randomness influences the game process enormously. In this article, we propose an optimized algorithm named Quick Neural Network Tree Search (QNNTS) based on deep reinforcement learning and Monte Carlo tree search (MCTS) to construct the artificial intelligence agent of EWN. Meanwhile, the lightness of the model makes it possible to train with much less computing resources. The optimization structure of the algorithm based on MCTS is named Optimized Upper Confidence Bound Applied to Tree with Heuristic Search, which introduces the expectation valuation strategy into the MCTS. As the prerequisite product of QNNTS, it performs with an improvement of the winning rate. Ultimately, the Attention-ResNet structure combined with domain knowledge is used to obtain the proposed algorithm. Compared with several conventional algorithms, it gains high winning rates of at least 68%.
Published in: IEEE Transactions on Games ( Volume: 16, Issue: 3, September 2024)
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- IEEE Keywords
- Index Terms
- Deep Learning ,
- Deep Reinforcement Learning ,
- Tree Search ,
- Monte Carlo Tree ,
- Monte Carlo Tree Search ,
- Neural Network ,
- Cognitive Domains ,
- Computational Resources ,
- Heuristic Search ,
- Left Side ,
- Upper Bound ,
- Deep Neural Network ,
- Evaluation Of Function ,
- Attention Mechanism ,
- State Value ,
- Polynomial Regression ,
- Rules Of The Game ,
- Position Values ,
- Stochastic Algorithm ,
- Game Of Chess ,
- Baseline Algorithms ,
- Gameplay ,
- Artificial Intelligence Research ,
- Upper Confidence Bound
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Deep Learning ,
- Deep Reinforcement Learning ,
- Tree Search ,
- Monte Carlo Tree ,
- Monte Carlo Tree Search ,
- Neural Network ,
- Cognitive Domains ,
- Computational Resources ,
- Heuristic Search ,
- Left Side ,
- Upper Bound ,
- Deep Neural Network ,
- Evaluation Of Function ,
- Attention Mechanism ,
- State Value ,
- Polynomial Regression ,
- Rules Of The Game ,
- Position Values ,
- Stochastic Algorithm ,
- Game Of Chess ,
- Baseline Algorithms ,
- Gameplay ,
- Artificial Intelligence Research ,
- Upper Confidence Bound
- Author Keywords