As an important part of my thesis I have been reading and learning about this subject. My effort has been split between an existing implementation in the sandbox, and working on a new implementation that can take advantage of a regression tree a fellow sandbox AI programmer has been working on.

For my first part I loaded the step animations I had created, and modified the reward function of the existing RL solution in order to achieve my desired behaviour. The demo works by simply having Marvin (sandbox star character) react to the mouse cursor. For me this meant stepping away and to the side of the cursor.

After being pretty happy with the result I was presented with the opportunity to work on a new implementation with the primary source of inspiration being  a paper on Real-Time Planning for Parameterized Human Motion by Wan-Yen Lo and Matthias Zwicker. Following this and eventually ending up doing things pretty differently a new implementation never the less was completed. The biggest difference from the former implementation is that with a regression tree you get a continues space for each dimension, and can use several dimensions.

Going to try to take some footage of the result at a later date, closer to the end of the thesis work.

Some links for Reinforcement Learning:

http://webdocs.cs.ualberta.ca/~sutton/RL-FAQ.html

http://aigamedev.com/open/reviews/planning-parameterized-human-motion/

Richard