A note on the representational incompatibility of function approximation and factored dynamics

We establish a new hardness result that shows that the difficulty of planning in factored Markov decision processes is representational rather than just computational. More precisely, we give & fixed family of factored MDPs with linear rewards whose optimal policies and value functions simply cannot be represented succinctly in any standard parametric form. Previous hardness results indicated that computing good policies from the MDP parameters was difficult, but left open the possibility of succinct function approximation for any fixed factored MDP. Our result applies even to policies which yield a polynomially poor approximation to the optimal value, and highlights interesting connections with the complexity class of Arthur-Merlin games.