摘要翻译:
将POMDP环境中的强化学习与常规混合概率逻辑程序相结合,提出了一个强化学习的概率逻辑程序框架,该框架具有描述特定领域知识的概率答案集语义。我们正式证明了我们的方法的正确性。我们证明了在我们的方法中为强化学习问题寻找策略的复杂性是NP完全的。另外,我们证明了任何强化学习问题都可以编码为具有答案集语义的经典逻辑程序。我们还证明了一个强化学习问题可以编码为SAT问题。我们提出了一种新的高级动作描述语言,它允许POMDP的因数表示。此外,我们对POMDP模型进行了改进,使其能够区分知识生产行为和改变环境的行为。
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英文标题:
《Reinforcement Learning in Partially Observable Markov Decision Processes
using Hybrid Probabilistic Logic Programs》
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作者:
Emad Saad
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最新提交年份:
2010
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要:
We present a probabilistic logic programming framework to reinforcement learning, by integrating reinforce-ment learning, in POMDP environments, with normal hybrid probabilistic logic programs with probabilistic answer set seman-tics, that is capable of representing domain-specific knowledge. We formally prove the correctness of our approach. We show that the complexity of finding a policy for a reinforcement learning problem in our approach is NP-complete. In addition, we show that any reinforcement learning problem can be encoded as a classical logic program with answer set semantics. We also show that a reinforcement learning problem can be encoded as a SAT problem. We present a new high level action description language that allows the factored representation of POMDP. Moreover, we modify the original model of POMDP so that it be able to distinguish between knowledge producing actions and actions that change the environment.
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PDF链接:
https://arxiv.org/pdf/1011.5951