DouRN: Improving DouZero by Residual Neural Networks

• URL: http://arxiv.org/abs/2403.14102v1
• Date: Thu, 21 Mar 2024 03:25:49 GMT
• Title: DouRN: Improving DouZero by Residual Neural Networks
• Authors: Yiquan Chen, Yingchao Lyu, Di Zhang,
• Abstract summary: Doudizhu is a card game that combines elements of cooperation and confrontation, resulting in a large state and action space. In 2021, a Doudizhu program called DouZero surpassed previous models without prior knowledge by utilizing traditional Monte Carlo methods and multilayer perceptrons. Our findings demonstrate that this model significantly improves the winning rate within the same training time.
• Score: 1.6013543712340956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep reinforcement learning has made significant progress in games with imperfect information, but its performance in the card game Doudizhu (Chinese Poker/Fight the Landlord) remains unsatisfactory. Doudizhu is different from conventional games as it involves three players and combines elements of cooperation and confrontation, resulting in a large state and action space. In 2021, a Doudizhu program called DouZero\cite{zha2021douzero} surpassed previous models without prior knowledge by utilizing traditional Monte Carlo methods and multilayer perceptrons. Building on this work, our study incorporates residual networks into the model, explores different architectural designs, and conducts multi-role testing. Our findings demonstrate that this model significantly improves the winning rate within the same training time. Additionally, we introduce a call scoring system to assist the agent in deciding whether to become a landlord. With these enhancements, our model consistently outperforms the existing version of DouZero and even experienced human players. \footnote{The source code is available at \url{https://github.com/Yingchaol/Douzero_Resnet.git.}

Related papers

• AlphaDou: High-Performance End-to-End Doudizhu AI Integrating Bidding [6.177038245239759]
  This paper modifies the Deep Monte Carlo algorithm framework by using reinforcement learning to obtain a neural network that simultaneously estimates win rates and expectations. The modified algorithm enables the AI to perform the full range of tasks in the Doudizhu game, including bidding and cardplay.
  arXiv  Detail & Related papers  (2024-07-14T17:32:36Z)
• Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models [0.0]
  We train a GPT model on Othello games and find that the model learned an internal representation of the board state. We extend this work into the more complex domain of chess, training on real games and investigating our model's internal representations. Unlike Li et al.'s prior synthetic dataset approach, our analysis finds that the model also learns to estimate latent variables like player skill to better predict the next character.
  arXiv  Detail & Related papers  (2024-03-21T18:53:23Z)
• DanZero: Mastering GuanDan Game with Reinforcement Learning [121.93690719186412]
  Card game AI has always been a hot topic in the research of artificial intelligence. In this paper, we are devoted to developing an AI program for a more complex card game, GuanDan. We propose the first AI program DanZero for GuanDan using reinforcement learning technique.
  arXiv  Detail & Related papers  (2022-10-31T06:29:08Z)
• Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning [95.78031053296513]
  No-press Diplomacy is a complex strategy game involving both cooperation and competition. We introduce a planning algorithm we call DiL-piKL that regularizes a reward-maximizing policy toward a human imitation-learned policy. We show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL.
  arXiv  Detail & Related papers  (2022-10-11T14:47:35Z)
• WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models [91.92346150646007]
  In this work, we introduce WinoGAViL: an online game to collect vision-and-language associations. We use the game to collect 3.5K instances, finding that they are intuitive for humans but challenging for state-of-the-art AI models. Our analysis as well as the feedback we collect from players indicate that the collected associations require diverse reasoning skills.
  arXiv  Detail & Related papers  (2022-07-25T23:57:44Z)
• DouZero+: Improving DouDizhu AI by Opponent Modeling and Coach-guided Learning [121.93690719186412]
  DouDizhu, a popular card game in China, is very challenging due to the imperfect information, large state space, elements of collaboration and a massive number of possible moves from turn to turn. Recently, a DouDizhu AI system called DouZero has been proposed. Trained using traditional Monte Carlo method with deep neural networks and self-play procedure without the abstraction of human prior knowledge. In this work, we propose to enhance DouZero by introducing opponent modeling into DouZero. Besides, we propose a novel coach network to further boost the performance of DouZero and accelerate its training process.
  arXiv  Detail & Related papers  (2022-04-06T03:18:17Z)
• DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning [65.00325925262948]
  We propose a conceptually simple yet effective DouDizhu AI system, namely DouZero. DouZero enhances traditional Monte-Carlo methods with deep neural networks, action encoding, and parallel actors. It was ranked the first in the Botzone leaderboard among 344 AI agents.
  arXiv  Detail & Related papers  (2021-06-11T02:45:51Z)
• Discovering Multi-Agent Auto-Curricula in Two-Player Zero-Sum Games [31.97631243571394]
  We introduce a framework, LMAC, that automates the discovery of the update rule without explicit human design. Surprisingly, even without human design, the discovered MARL algorithms achieve competitive or even better performance. We show that LMAC is able to generalise from small games to large games, for example training on Kuhn Poker and outperforming PSRO.
  arXiv  Detail & Related papers  (2021-06-04T22:30:25Z)
• Deep Policy Networks for NPC Behaviors that Adapt to Changing Design Parameters in Roguelike Games [137.86426963572214]
  Turn-based strategy games like Roguelikes, for example, present unique challenges to Deep Reinforcement Learning (DRL) We propose two network architectures to better handle complex categorical state spaces and to mitigate the need for retraining forced by design decisions.
  arXiv  Detail & Related papers  (2020-12-07T08:47:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.