论文阅读15：Massively Parallel Methods for Deep Reinforcement Learning
论文阅读笔记之Massively Parallel Methods for Deep Reinforcement Learning 本文创新点：提出了一个对于深度强化学习的大规模分布式架构Gorila（General Reinforcement learning Architecture） 架构组成：用于产生behavior的并行actor组； 从经验池学习的并行learner组； 代表值函数
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相关文章
- 1. 【5分钟 Paper】Asynchronous Methods for Deep Reinforcement Learning
- 2. 论文笔记之：Asynchronous Methods for Deep Reinforcement Learning
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- 4. 论文阅读：DRN: A Deep Reinforcement Learning Framework for News Recommendation
- 5. RL论文阅读【三】Dueling Network Architectures for Deep Reinforcement Learning
- 6. 论文阅读：(LIRD)Deep Reinforcement Learning for List-wise Recommendations
- 7. Deep Reinforcement Learning for Dialogue Generation 论文阅读 A Diversity-Promoting Objective Function fo
- 8. 论文阅读-《Learning Deep Features for Discriminative Localization》
- 9. 论文阅读：《Wide & Deep Learning for Recommender Systems》
- 10. 【论文阅读】Deep Residual Learning for Image Recognition