Can we use a stochastic policy in DDPG framework?

Web: https://www.reddit.com/r/reinforcementlearning/comments/ukbqmj/can_we_use_a_stochastic_policy_in_ddpg_framework/

In DDPG we use a deterministic policy, i.e. s--->a, each state has a deterministic action, but can we use a stochastic policy, i.e. the actions are sampled from a distribution. I think this might encourage exploration and thus can improve the performance. What do you think?

ddpg framework policy reinforcementlearning stochastic