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RL Learner：是一个分布式训练环境，并行从pool采样得到梯度，同步全部梯度取均值，更新策略后将策略传给AI Server。
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AI Server：涵盖了游戏环境和AI之间的交互逻辑，用来产生数据。即从游戏中收集state，预测英雄行为。在使用中，一台AI服务器绑定一个cpu内核。我们构建了快速推断库FeatherCNN，以来更有效的生成推断模型。开源地址：github.com/Tencent/Feat
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Dispatch Module：从多个AI server搜集数据并压缩、打包奥、传送到Memory
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Memory Pool：也是服务器。它的内部实现为内存高效的循环队列，用于数据存储。它支持各种长度的样本以及基于生成时间的数据样本
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状态设计：如上图；将图像特征fi，向量特征fu和游戏状态信息fg（可观察到的游戏状态）分别通过卷积层、最大池化层和全连接层编码。LSTM输出动作按钮和移动方向。
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动作解耦：认为动作之间独立，目标为最终几个策略累积奖励之和；
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初始随机动作产生数据；
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action mask：根据专家经验去掉明显不合理、受限制的动作；
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dual-PPO：原始PPO在Advantage小于0的时候也容易产生大的策略梯度，作者改进了PPO，使其支持大范围的数据训练。
我们训练的AI强化学习跟人类顶级玩家进行1v1的竞赛
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