Mastering Complex Control in MOBA Games with Deep Reinforcement Learning（论文笔记）
本文由腾讯AI Lab跟天美发表，用于王者荣耀1v1的AI训练，达成99.81%的胜率。文章分了几个部分进行讲解。 大系统 整个框架分为四个模块：RL Learner，AI Server，Dispatch module与Memory Pool，如下图： AI Server：此模块用当前的agent与游戏环境进行交互来收集数据，一个AI Server绑定一个cpu，agent会copy到cpu中，为
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相关文章
- 1. 论文笔记：《Playing Atari with Deep Reinforcement Learning》
- 2. Continuous control with Deep Reinforcement Learning
- 3. 【5分钟 Paper】Continuous Control With Deep Reinforcement Learning
- 4. 论文笔记4：Language Understanding for Text-based Games using Deep Reinforcement Learning
- 5. Playing Atari with Deep Reinforcement Learning
- 6. 论文笔记 Reinforcement Learning with Derivative-Free Exploration
- 7. DDPG，CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING 论文阅读
- 8. 解读continuous control with deep reinforcement learning（DDPG）
- 9. 【论文翻译】Playing Atari with Deep Reinforcement Learning
- 10. 论文笔记——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING