Card Game Models
DouZero
Introduced by Zha et al. in DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement LearningDouZero 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.
Source: DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Card Games | 1 | 25.00% |
| Game of Poker | 1 | 25.00% |
| Multi-agent Reinforcement Learning | 1 | 25.00% |
| Reinforcement Learning (RL) | 1 | 25.00% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
DQN
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Q-Learning Networks | |
Feedforward Network
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Feedforward Networks | |
LSTM
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Recurrent Neural Networks |
