Value Determination with General Function Approximators
Vassilis Papavassiliou, Stuart Russell · UC Berkeley · 2000
A new algorithm is described for value determination in Markov decision processes. The algorithm works with arbitrary approximate representations of the value function. We show that if the approximating family is agnostically PAC-learnable, then the algorithm converges to a solution that is close to the globally optimal solution in the approximating family.