A thorough understanding of on-policy and off-policy in Reinforcement learning
一句话区分on-policy and off-policy： 看behaviour policy和current policy是不是同一个就OK了！ 我这篇文章主要想借着理解on-policy和off-policy的过程来加深对其他RL算法的认识。因为万事万物总是相互联系的，所以在自己探究，琢磨为什么有些算法是on-policy或者off-policy的过程中，对于它们的本质也有了更深的认识。 首
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相关文章
- 1. Policy in Reinforcement Learning
- 2. （转）Applications of Reinforcement Learning in Real World
- 3. Application of Opposition-Based Reinforcement Learning in Image Segmentation
- 4. What’s New in Deep Learning Research: Understanding Meta-Learning
- 5. Reinforcement learning and Deep learning
- 6. Reinforcement Learning in Continuous State and Action Spaces: A Brief Note
- 7. Semantic Segmentation: A thorough Review
- 8. A Unified Game-Theoretic Approach to Multi-agent Reinforcement Learning
- 9. Imitation Learning | A brief overview of Imitation Learning
- 10. Reinforcement Learning, Fast and Slow