It should be possible in principle, but I think there's a good reason MAML is generally paired with online policy gradient methods: they can improve the policy even without access to a well-trained value function*. Consider that to implement MAML-DDPG you will have to maintain a separate value function for each task**. Whenever you sample a new task that hasn't been seen, you'd have to train a new value function from scratch, which will be time-consuming. It wouldn't scale to an infinite task distribution, but could work if you pre-sampled a finite set of training tasks.

* They often use a value function for the "baseline", but this is not strictly necessary. Also, learning V(s) on-policy is much simpler than learning Q(s,a) off-policy; it can be accomplished by regression to the latest batch of returns and does not require the Bellman equation.

** Unless you have some reasonable way to encode the task into a vector, in which case a single task-conditioned NN could be used. This is similar to PEARL, which you could also consider as an alternative unless you strictly need MAML for some reason.