In this chapter, I examine other approaches to exploration that could be combined with texplore’s model. First, I introduce three domain classes that each suggest a different type of exploration. Then, in Section 6.1, I look at how to perform exploration in domains where a needle-in-a-haystack search is required to find an arbitrarily located reward or transition. In the next section, I look at the opposite case: can we explore better in a domain with a richer, more informative set of state features? Finally, in Section 6.3, I present an algorithm that can learn which of these exploration approaches to adopt on-line, while interacting with the environment. Then I present some empirical comparisons of these approaches against texplore in Section 6.4, before summarizing the chapter in Section 6.5.


Reward Function Exploration Strategy Random Forest Model Intrinsic Reward External Reward 
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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Department of Computer Science University of Texas at AustinAustinUSA

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