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About Algorithms for Reinforcement Learning
<p>强化学习是一种学习范式，涉及学习控制系统以最大化表达长期目标的数值性能度量。强化学习与监督学习的区别在于，仅向学习者提供有关学习者预测的部分反馈。此外，预测可能会通过影响受控系统的未来状态而产生长期影响。因此，时间起着特殊的作用。强化学习的目标是开发高效的学习算法，以及了解算法的优点和局限性。强化学习引起了极大的兴趣，因为它可以用来解决大量的实际应用，从人工智能问题到运筹学或控制工程。在本书中，我们关注那些建立在强大的动态规划理论之上的强化学习算法。我们给出了一个相当全面的学习问题目录，描述了核心思想以及大量最先进的算法，然后讨论了它们的理论性质和局限性。</p>
Detailed Information
| Author: | Csaba Szepesvari |
|---|---|
| Publication Year: | 2009 |
| Pages: | 98 |
| Language: | other |
| File Size: | 1.8754 |
| Format: | |
| Price: | FREE |
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