Calculating the reward-rate manifold in the Gaussian-MSE case from Resource-rational reinforcement learning and sensorimotor causal states, and resource-rational maximiners

Calculates a reward-rate manifold when statistics are Gaussian and the reward function is a squared error. In this case, the generalized Blahut-Arimoto algorithm massively simplifies, and we can use the equations in Appendix D to find Fig 1.