Methods › Reinforcement Learning › Card Game Models › DouZero

DouZero

4 papers tagged archive 2025-07-28

Introduced by Daochen Zha et al. in DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

DouZero is an AI system for the card game DouDizhu that enhances traditional Monte-Carlo methods with deep neural networks, action encoding, and parallel actors. The Q-network of DouZero consists of an LSTM to encode historical actions and six layers of MLP with hidden dimension of 512. The network predicts a value for a given state-action pair based on the concatenated representation of action and state.

PaperSource

Papers archive 2025-07-28

4 shown of 4, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Deep Reinforcement Learning3
Card Games2
Game of Poker1
Multi-agent Reinforcement Learning1
Reinforcement Learning1
Reinforcement Learning (RL)1
reinforcement-learning1

Usage over time archive 2025-07-28

Papers per year tagged with DouZero: 2021 to 2024, peak 2 2 0 2021: 1 paper 2021 2022: 1 paper 2022 2023: 0 papers 2023 2024: 2 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (4 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Card Game Models

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