Is DDPG just for deterministic environments? I want to develop an AI for continuous space. I reached to DDPG algorithm that takes actions deterministically. 

If DDPG takes actions deterministically, should the environment also be deterministic? I want non-deterministic, continuous real-world environments. Is DDPG the algorithm I am looking for? Is there any other algorithm for my need?
I am not an expert in this area. But I believe that the word "Deterministic" is for "Policy" in the "Deterministic Policy" Gradient. It does not mean  deterministic environment.

Stochastic policy: Probabilistic(random) action choice for a given state.
Deterministic policy: one action is chosen for a given state. 

Deterministic Policy Gradient algorithm still can handle a stochastic (and continuous, of cause) environment, but the policy will be deterministic. 

Reference  

"in DDPG, the Actor directly maps states to actions (the output of the network directly the output) instead of outputting the probability distribution across a discrete action space"  -towards Data Science  

Deterministic Policy Gradient Algorithms by Silver et al. PDF