<story><title>Genetic Algorithm Walkers</title><url>http://rednuht.org/genetic_walkers/</url></story><parent_chain></parent_chain><comment><author>Animats</author><text>I&amp;#x27;m currently stuck at 1233 after 250 generations. The last improvement was at gen 183. Walking gets part way down the first downslope, then falls. The current champions are far better than any of their mutations, so a local maximum has been reached. This is the fate of most evolutionary AI.&lt;p&gt;Once on the downslope, the controller for flat ground won&amp;#x27;t work, so now it has to jump to something substantially more complex to progress.  Making such jumps was an unsolved problem with genetic algorithms 20 years ago, and I commented back then that whoever solved it would win a Nobel Prize.  Still waiting.&lt;p&gt;I suspect that a solution lies in work on neural nets where one net is trained, then a second, smaller, net is trained to match the first. This is a form of &amp;quot;abstraction&amp;quot;.  Somewhere in that space is something very important to AI.</text></comment>
<story><title>Genetic Algorithm Walkers</title><url>http://rednuht.org/genetic_walkers/</url></story><parent_chain></parent_chain><comment><author>sinwave</author><text>I suspect that the best way to tackle this problem is to treat &amp;quot;learning to walk&amp;quot; as a reinforcement learning problem. This way you can evaluate actions within each episode rather than waiting until the end of each trial to adjust parameters encoding the walking strategy.&lt;p&gt;As karpathy suggests below (and he&amp;#x27;s certainly much more qualified than me), an evolutionary method such as GA&amp;#x27;s, while apparently fairly effective, could well be wasting valuable information learned in real time through interaction.</text></comment>