# DouRN: Improving DouZero by Residual Neural Networks

arXiv: https://arxiv.org/abs/2403.14102 · HTML: https://mlsanity.com/paper/2403.14102/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning in Large Discrete Action Spaces](https://mlsanity.com/paper/1512.07679/) (2015)
- [DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning](https://mlsanity.com/paper/2106.06135/) (2021)
- [Deep Residual Reinforcement Learning](https://mlsanity.com/paper/1905.01072/) (2019)
- [DouZero+: Improving DouDizhu AI by Opponent Modeling and Coach-guided Learning](https://mlsanity.com/paper/2204.02558/) (2022)

## Abstract (arXiv)

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 DouZerocitezha2021douzero 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. footnoteThe source code is available at urlhttps://github.com/Yingchaol/DouzeroResnet.git.