Returns test and subset. Code for layered feed-forward networks Network is represented as a list of lists of units. Perceptron updating - simple version without lower bound on delta Hertz, Krogh, and Palmer, eq. Given an MDP, determine the q-values of the states. MUST be the old value not the new.
Target is encoded as a decision list.
Passive, stochastic 4x3 environment for chapter 20. When a given training sequence terminates, update the utility of each state visited in the sequence to reflect the rewards received from then on. Thus, the main job is to update the model over time. Being a passive agent, it simply does no-op at each step, watching the world go by. Fairly tedious because of initializing new transition records.