How DDPG (Deep Deterministic Policy Gradient) Algorithms works in reinforcement learning ?

DDPG (Deep Deterministic Policy Gradient) is a model-free off-policy reinforcement learning algorithm for learning continuous actions. It combines ideas from DPG (Deterministic Policy Gradient) and DQN (Deep Q-Network). The development of deep deterministic policy gradient (DDPG) was inspired by the success of DQN and is aimed to improve performance for tasks that requires a continuous action space. DDPG incorporates an actor-critic approach based on DPG. The algorithm uses two neural networks, one for the actor and one for the critic.