  This paper gives specific divergence examples of value-iteration for several
major Reinforcement Learning and Adaptive Dynamic Programming algorithms, when
using a function approximator for the value function. These divergence examples
differ from previous divergence examples in the literature, in that they are
applicable for a greedy policy, i.e. in a "value iteration" scenario. Perhaps
surprisingly, with a greedy policy, it is also possible to get divergence for
the algorithms TD(1) and Sarsa(1). In addition to these divergences, we also
achieve divergence for the Adaptive Dynamic Programming algorithms HDP, DHP and
GDHP.
