According to the Sutton book this might be better described as “REINFORCE with baseline” (page 342) 13 rather than actor-critic:

Although the REINFORCE-with-baseline method learns both a policy and a state-value function, we do not consider it to be an actor-critic method because its state-value function is used only as a baseline, not as a critic. That is, it is not used for bootstrapping (updating a state from the estimated values of subsequent states), but only as a baseline for the state being updated.

But it does perform pretty well. The rewards are given only at the end of the episode, and the discount factor was lowered because episodes are relatively short (~50 steps).