Using MDP Characteristics to Guide Exploration in Reinforcement Learning

14 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Independently Controllable Features (2017, background): “Such ideas have only been explored brieﬂy in the literature, e.g. Ratitch & Precup (2003) 4 Discussion There is a large body of work on learning features in RL focusing on indirectly learning good internal representations.”
Independently Controllable Factors (2017, background): “…process could be driven by a notion of controllability, predicting the interestingness of objects in a scene and choosing features and associated policies with which to attempt controlling them – such ideas have only been brieﬂy explored in the literature (e.g. Ratitch and Precup (2003)).”
Sample Complexity Bounds of Exploration (2012, methodology): “…techniques exist that guide exploration using interval estimation (Kaelbling, 1993; Meuleau and Bourgine, 1999; Wiering and Schmidhuber, 1998), information gain (Dearden et al, 1999), MDP characteristics (Ratitch and Precup, 2002), or expert demonstration (Abbeel and Ng, 2005), to name a few.”
An information-theoretic approach to curiosity-driven reinforcement learning (2012, background): “[25,13]) A different strategy, which yields interesting theoretical results, is that of optimism in the face of uncertainty: if a state has not been visited sufficiently for the agent to be familiar with it, it is automatically considered good, so the agent will be driven towards it.”