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We consider the problem of finding good finite-horizon policies for POMDPs under the expected reward metric. The policies con sidered are free finite-memory policies with limited memory; a policy is a mapping from the space of observation-memory pairs to the space of action-memory pairs (the policy up dates the memory as it goes), and the num ber of possible memory states is a parameter of the input to the policy-finding algorithms. The algorithms considered here are prelimi nary implementations of three search heuris tics: local search, simulated annealing, and genetic algorithms. We compare their out comes to each other and to the optimal poli cies for each instance. We compare run times of each policy and of a dynamic programming algorithm for POMDPs developed by Hansen that iteratively improves a finite-state con troller the previous state of the art for finite memory policies. The value of the best policy can only improve as the amount of memory increases, up to the amount needed for an optimal finite-memory policy. Our most surprising finding is that more memory helps in another way: given more memory than is needed for an optimal policy, the algo rithms are more likely to converge to optimal valued policies.
My Brain is Full: When More Memory Helps