Solving large unknown POMDPs is an open research problem. Policy search is one solution method that is attractive as it scales in the size of the policy, which is typically much simpler than the environment. We present a global search algorithm capable of finding good policies for POMDPs that are substantially larger than previously reported results. Our algorithm is general; we show it can be used with, and improves the performance of, existing local search techniques such as gradient ascent. Sharing information between the members of the population is the key to our algorithm and we show it results in better performance than equivalent parallel searches that do not share information. Unlike previous work our algorithm does not require the size of the policy to be known in advance.
|Title of host publication||Recent Advances in Reinforcement Learning|
|Publication status||Published - 27 Nov 2008|