Entries for August 2006
What to try next with coevolution
I'm waiting for a lot of experiments to finish, but interim results from the coevolutionary runs continue to look as unimpressive as those I described at the end of my last post. I'll let the current crop run out for another day, but meanwhile I need to come up with some hypotheses about why these runs are doing so badly, and tests for those hypotheses. This can guide the experiments I set up tomorrow and leave running over the weekend. Ideas after the cut.
Continue reading "What to try next with coevolution"
Looking more closely at individual agents
While I wait for experiments to finish, it's time to take a closer look at those agents I've found so far that seem to be performing well. For many reasons, the performance score during a run is an imperfect gauge of how good the agent actually is, so I like to score them all by presenting an identical set of 1000 fresh trials, and then look closely at their performance on those trials for which they do badly as a guide to what strategy the agents are actually implementing.
I'll be going into a lot of detail on small things, so the report of results is behind the cut.
Continue reading "Looking more closely at individual agents"
Co-evolution, step 1
I'm working on the to-do list I put up last week. I now have a rudimentary system for co-evolution of trials, and some experiments are running with it. Results using the most simplistic scheme possible have been terrible, but this is not surprising and I know what to try next.
Continue reading "Co-evolution, step 1"
To-do list
I'm getting married at the end of the month, and the wedding and honeymoon between them will take about 3 weeks out of work, while there's a conference deadline not long after I'll be back from the honeymoon. So it's time for a list of things I hope to get done in the coming 3½ weeks, to keep myself focussed.
Bloomington debrief
Last week I went to Bloomington to catch up with my lab. It was a shorter visit than would have been ideal, because of external constraints, but we got a lot done in two long, busy days. In terms of my own work, we mostly talked about ways I can make the task easier, because at the moment it takes too many hours of CPU time to get one good agent.
There's a bit of a tension here between the results I need in the end and the results I need to take the next steps with my PhD. Because I'm explicitly asking about the conditions under which learning does and does not evolve, all of the negative results are actually relevant in the long run, but for now I need to chase after positives. This is so that I can defend a research proposal—the sooner the better—which will be impossible without some positive results. If I can demonstrate some conditions under which good agents reliably evolve, then it's not too hard to argue that I'll be able to find many manipulations that cause them to fail, but the reverse is a lot trickier. So I'm left looking for ways of making the task easier.
Specific ideas I got from colleagues and want to take note of are behind the cut.