In this month, I continued my studies in Relational Reinforcement Learning by reviewing the article Towards Informed Reinforcement Learning from the proceedings of the 2004 Machine Learning workshop of Relational Reinforcement Learning.   Basically the articles summarizes that an agent with limited information can find an optimal policy and can achieve a goal or goal states with limited information about its environment.  The experiments reported seems to suggest this type of exploration is possible.  According to Google Scholar search, there are 11 subsequent articles that reference this one.  In the RRL arena, my goal is to repeat the block’s world experiment as reported in Relational Reinforcement Learning article by Dzeroski, De Raedt, and Blockeel.

Also, I have been focused on Robert Winkler’s book An Introduction to Bayesian Analysis and Inference.  From the Winkler book, my goal is to gain a better understanding of Bayesian Inference to better understand the Decision Theoretic models of machine learning.  In the machine learning world, Bayesian approaches to various problems are resulting interesting solutions to various problems in Multiagent Reinforcement Learning.

There are some interesting articles in the Journal of Bayesian Analysis and in JAIR.

In another topic of interest, I am continuing my reintroduction of LISP by reading the Patrick Winston and Berthold Horn classic.

From a statistical point of view, this month achieve a new record number of hits with 806 hits, boosting my total for the current year to over 3200 hits.  The hotest pages are my About page, followed by Wumpus World and Wumpus World Revisited.

Thank you for your interest and support.