Information Theory of Decisions and Actions

Using a graphical model, a recursive Bellman optimality equation for information measures is derived, in analogy to reinforcement learning; from this, new algorithms for calculating the optimal trade-off between the value-to-go and the required information- to-go are obtained, unifying the ideas behind the Bellman and the Blahut–Arimoto iterations.