PR10.10:#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
What’s problem? Count-based exploration algorithms are known to perform near-optimally when used in conjunction with tabular reinforcement learning (RL) methods for solving small discrete Markov decis
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相關文章
- 1. Building a ROS simulation platform for Deep Reinforcement Learning research ...
- 2. Deep Reinforcement Learning
- 3. Deep Reinforcement Learning for Dialogue Generation
- 4. Deep Reinforcement Learning for Join Order Enumeration
- 5. Deep Reinforcement Learning for List-wise Recommendations
- 6. 【5分鐘 Paper】Asynchronous Methods for Deep Reinforcement Learning
- 7. Deep Reinforcement Learning with a Natural Language Action Space
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- 9. Neuroevolution: A different kind of deep learning
- 10. Relational Deep Reinforcement Learning