Policy Explanation in Factored Markov Decision Processes

By Francisco Elizalde, E. Sucar. 2008

How researchers see this paper

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

1. Song Bai
2. Li Zhang
3. Zhao Yang
4. Bradley Hayes
5. Philip H. S. Torr
6. Julie Arnold Shah
7. Anurag Koul
8. Sam Greydanus

Citations in the corpus

• Improving Robot Controller Transparency Through Autonomous Policy Explanation (2017, background): “[10] detailed a system for articulating the primary decision factors of a program, helping to direct an operator’s attention to minimize failures or surprises.”
• Visualizing and Understanding Atari Agents (2017, background): “Their key contribution is a method of approximating the observed behavior of deep RL policies via "Semi-Aggregated" Markov Decision Processes (SAMDPs), which are used to gain insights about the deep RL policies.”
• Learn to Interpret Atari Agents (2018, background): “Interpreting RL systems traditionally involves language generation via first-order logic (Dodson et al., 2011; Elizalde et al., 2008; Khan et al., 2009; Hayes & Shah, 2017).”
• The Impact of POMDP-Generated Explanations on Trust and Performance in Human-Robot Teams (2016, background): “While most of this work operated on rule- and logic-based systems, there has been more recent work on generating explanations based on Markov Decision Problems (MDPs) [8, 15].”