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README
Apache-2.0

[ICML 2021] DouZero: 从零开始通过自我博弈强化学习来学打斗地主

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English README

DouZero是一个为斗地主设计的强化学习框架。斗地主十分具有挑战性。它包含合作、竞争、非完全信息、庞大的状态空间。斗地主也有非常大的状态空间,并且每一步合法的牌型会非常不一样。DouZero由快手AI平台部开发。

社区:

  • Slack: 加入 DouZero 频道.
  • QQ群: 加入我们的QQ群819204202. 密码: douzeroqqgroup
Demo

引用

如果您用到我们的项目,请添加以下引用:

Zha, Daochen, et al. "DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning." arXiv preprint arXiv:2106.06135 (2021).

@article{zha2021douzero,
  title={DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning},
  author={Zha, Daochen and Xie, Jingru and Ma, Wenye and Zhang, Sheng and Lian, Xiangru and Hu, Xia and Liu, Ji},
  journal={arXiv preprint arXiv:2106.06135},
  year={2021}
}

为什么斗地主具有挑战性

除了非完全信息带来的挑战外,斗地主本身也包含巨大的状态和动作空间。具体来说,斗地主的动作空间大小高达10^4(详见该表格)。不幸的是,大部分强化学习算法都只能处理很小的动作空间。并且,斗地主的玩家需要在部分可观测的环境中,与其他玩家对抗或合作,例如:两个农民玩家需要作为一个团队对抗地主玩家。对对抗和合作同时进行建模一直以来是学术界的一个开放性问题。

在本研究工作中,我们提出了将深度蒙特卡洛(Deep Monte Carlo, DMC)与动作编码和并行演员(Parallel Actors)相结合的方法,为斗地主提供了一个简单而有效的解决方案,详见我们的论文

安装

训练部分的代码是基于GPU设计的,因此如果想要训练模型,您需要先安装CUDA。安装步骤可以参考本教程。对于评估部分,CUDA是可选项,您可以使用CPU进行评估。

首先,克隆本仓库(如果您访问Github较慢,国内用户可以使用Gitee镜像):

git clone https://github.com/kwai/DouZero.git

确保您已经安装好Python 3.6及以上版本,然后安装依赖:

cd douzero
pip3 install -r requirements.txt

我们推荐通过以下命令安装稳定版本的Douzero:

pip3 install douzero

如果您访问较慢,国内用户可以通过清华镜像源安装:

pip3 install douzero -i https://pypi.tuna.tsinghua.edu.cn/simple

或是安装最新版本(可能不稳定):

pip3 install -e .

我们不建议用Windows系统进行训练或评估。Windows系统可能会遇到些问题,详见Windows下的问题。但Windows用户仍可以使用多线程进行模型评估,并且在本地运行演示。如果你发现解决该问题的方法,请联系我们!

训练

假定您至少拥有一块可用的GPU,运行

python3 train.py

这会使用一块GPU训练DouZero。如果需要用多个GPU训练Douzero,使用以下参数:

  • --gpu_devices: 用作训练的GPU设备名
  • --num_actors_devices: 被用来进行模拟(如自我对弈)的GPU数量
  • --num_actors: 每个设备的演员进程数
  • --training_device: 用来进行模型训练的设备

例如,如果我们拥有4块GPU,我们想用前3个GPU进行模拟,每个GPU拥有15个演员,而使用第四个GPU进行训练,我们可以运行以下命令:

python3 train.py --gpu_devices 0,1,2,3 --num_actors_devices 3 --num_actors 15 --training_device 3

其他定制化的训练配置可以参考以下可选项:

--xpid XPID           实验id(默认值:douzero)
--save_interval SAVE_INTERVAL
                      保存模型的时间间隔(以分钟为单位)
--objective {adp,wp}  使用ADP或者WP作为奖励(默认值:ADP)
--gpu_devices GPU_DEVICES
                      用作训练的GPU设备名
--num_actor_devices NUM_ACTOR_DEVICES
                      被用来进行模拟(如自我对弈)的GPU数量
--num_actors NUM_ACTORS
                      每个设备的演员进程数
--training_device TRAINING_DEVICE
                      用来进行模型训练的设备
--load_model          读取已有的模型
--disable_checkpoint  禁用保存检查点
--savedir SAVEDIR     实验数据存储跟路径
--total_frames TOTAL_FRAMES
                      Total environment frames to train for
--exp_epsilon EXP_EPSILON
                      探索概率
--batch_size BATCH_SIZE
                      训练批尺寸
--unroll_length UNROLL_LENGTH
                      展开长度(时间维度)
--num_buffers NUM_BUFFERS
                      共享内存缓冲区的数量
--num_threads NUM_THREADS
                      学习者线程数
--max_grad_norm MAX_GRAD_NORM
                      最大梯度范数
--learning_rate LEARNING_RATE
                      学习率
--alpha ALPHA         RMSProp平滑常数
--momentum MOMENTUM   RMSProp momentum
--epsilon EPSILON     RMSProp epsilon

评估

评估可以使用GPU或CPU进行(GPU效率会高得多)。预训练模型可以通过Google Drive百度网盘, 提取码: 4624 下载。将预训练权重放到baselines/目录下。模型性能通过自我对弈进行评估。我们提供了一些其他预训练模型和一些启发式方法作为基准:

  • random: 智能体随机出牌(均匀选择)
  • rlcard: RLCard项目中的规则模型
  • SL (baselines/sl/): 基于人类数据进行深度学习的预训练模型
  • DouZero-ADP (baselines/douzero_ADP/): 以平均分数差异(Average Difference Points, ADP)为目标训练的Douzero智能体
  • DouZero-WP (baselines/douzero_WP/): 以胜率(Winning Percentage, WP)为目标训练的Douzero智能体

第1步:生成评估数据

python3 generate_eval_data.py

以下为一些重要的超参数。

  • --output: pickle数据存储路径
  • --num_games: 生成数据的游戏局数,默认值 10000

第2步:自我对弈

python3 evaluate.py

以下为一些重要的超参数。

  • --landlord: 扮演地主的智能体,可选值:random, rlcard或预训练模型的路径
  • --landlord_up: 扮演地主上家的智能体,可选值:random, rlcard或预训练模型的路径
  • --landlord_down: 扮演地主下家的智能体,可选值:random, rlcard或预训练模型的路径
  • --eval_data: 包含评估数据的pickle文件

例如,可以通过以下命令评估DouZero-ADP智能体作为地主对阵随机智能体

python3 evaluate.py --landlord baselines/douzero_ADP/landlord.ckpt --landlord_up random --landlord_down random

以下命令可以评估DouZero-ADP智能体作为农民对阵RLCard智能体

python3 evaluate.py --landlord rlcard --landlord_up baselines/douzero_ADP/landlord_up.ckpt --landlord_down baselines/douzero_ADP/landlord_down.ckpt

默认情况下,我们的模型会每半小时保存在douzero_checkpoints/douzero路径下。我们提供了一个脚本帮助您定位最近一次保存检查点。运行

sh get_most_recent.sh douzero_checkpoints/douzero/

之后您可以在most_recent_model路径下找到最近一次保存的模型。

Windows下的问题

如果你使用的是Windows系统,你可能遇到operation not supported错误。这是由于Windows系统不支持CUDA tensor上的多进程。但是,由于我们的代码是对GPU进行优化,有对CUDA tensor的大量操作。在评估中跑多进程也可能遇到问题。因此我们推荐使用Linux服务器或者macOS系统进行模型训练或评估。

核心团队

鸣谢

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[ICML2021] DouZero斗.地主AI:从零开始通过自我博弈强化学习来学打斗.地主 展开 收起
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