Can I add expert data to the replay buffer used by the DDPG algorithm in order to make it converge faster?

I am working on a restricted reinforcement learning environment, i.e. the environment breaks very often (i.e.: the communication between the simulator and reinforcement learning agent breaks after some time). So, it is getting difficult for me to continue training in this environment.

The continuous state-space is $$\mathcal{S} \subseteq \mathbb{R}^{10}$$ and the continuous action-space $$\mathcal{A} \subseteq \mathbb{R}^{2}$$.

What I want to know is whether I can add expert data to the replay buffer, given that DDPG is an off-policy algorithm?

Or I should go with the behavior cloning technique to train the actor-network only, so that it converges rapidly?

I just want to get the work done first and then I can think of exploring the environment.